Chapter 4 Diversity analysis

load("data/data.Rdata")

4.1 Alpha diversity

# Calculate Hill numbers
richness <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 0) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(richness = 1) %>%
  rownames_to_column(var = "sample")

neutral <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 1) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(neutral = 1) %>%
  rownames_to_column(var = "sample")

phylogenetic <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 1, tree = genome_tree) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(phylogenetic = 1) %>%
  rownames_to_column(var = "sample")

# Merge all metrics
alpha_div <- richness %>%
  full_join(neutral, by = join_by(sample == sample)) %>%
  full_join(phylogenetic, by = join_by(sample == sample))

4.1.1 Acclimation samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="1_Acclimation") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic"))) %>%
      ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#008080', "#d57d2c")) +
      scale_fill_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#00808050', "#d57d2c50")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.58) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

4.1.2 Antibiotics samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="2_Antibiotics") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic"))) %>%
      ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#008080', "#d57d2c")) +
      scale_fill_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#00808050', "#d57d2c50")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.58) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

4.1.3 Transplant_1 samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="3_Transplant1") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic"))) %>%
      ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA","#d57d2c", "#76b183")) +
      scale_fill_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA50","#d57d2c50","#76b18350")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.7) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

4.1.4 Transplant_2 samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="4_Transplant2") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic"))) %>%
      ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA","#d57d2c", "#76b183")) +
      scale_fill_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA50","#d57d2c50","#76b18350")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.7) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

4.1.5 Post-Transplant_1 samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="5_Post-FMT1") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic"))) %>%
      ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA","#d57d2c", "#76b183")) +
      scale_fill_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA50","#d57d2c50","#76b18350")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.7) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

4.1.6 Post-Transplant_2 samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="6_Post-FMT2") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic"))) %>%
      ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-control","Warm-control", "Cold-intervention"),
          values=c("#4477AA","#d57d2c", "#76b183")) +
      scale_fill_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-control","Warm-control", "Cold-intervention"),
          values=c("#4477AA50","#d57d2c50","#76b18350")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.7) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

4.2 Beta diversity

beta_q0n <- genome_counts_filt %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 0)

beta_q1n <- genome_counts_filt %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 1)

beta_q1p <- genome_counts_filt %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 1, tree = genome_tree)

4.3 Permanovas

4.3.0.1 Load required data

meta <- column_to_rownames(sample_metadata, "Tube_code")

4.3.1 1. Do the antibiotics work?

4.3.1.1 Antibiotics

treat1 <- meta  %>%
  filter(time_point == "2_Antibiotics")

temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL

treat1.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(treat1))]
identical(sort(colnames(treat1.counts)),sort(as.character(rownames(treat1))))

treat1_nmds <- sample_metadata %>%
  filter(time_point == "2_Antibiotics")

4.3.1.1.1 Number of samples used

[1] 23

beta_div_richness_treat1<-hillpair(data=treat1.counts, q=0)
beta_div_neutral_treat1<-hillpair(data=treat1.counts, q=1)
beta_div_phylo_treat1<-hillpair(data=treat1.counts, q=1, tree=genome_tree)

4.3.1.1.2 Richness

betadisper(beta_div_richness_treat1$S, treat1$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)  
Groups     1 0.015319 0.0153186 6.8764    999  0.021 *
Residuals 21 0.046782 0.0022277                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.016
Hot_dry  0.015919        

adonis2(formula=beta_div_richness_treat1$S ~ Population, data=treat1[labels(beta_div_neutral_treat1$S),], permutations=999) %>%
        as.matrix() %>%
        kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	1	1.356644	0.1527052	3.784762	0.001
Residual	21	7.527429	0.8472948	NA	NA
Total	22	8.884073	1.0000000	NA	NA

4.3.1.1.3 Neutral

betadisper(beta_div_neutral_treat1$S, treat1$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)  
Groups     1 0.030536 0.0305358 3.8593    999  0.074 .
Residuals 21 0.166158 0.0079123                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.061
Hot_dry  0.062842        

adonis2(formula=beta_div_neutral_treat1$S ~ Population, data=treat1[labels(beta_div_neutral_treat1$S),], permutations=999) %>%
        as.matrix() %>%
        kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	1	1.785669	0.2085055	5.532084	0.001
Residual	21	6.778468	0.7914945	NA	NA
Total	22	8.564137	1.0000000	NA	NA

4.3.1.1.4 Phylogenetic

betadisper(beta_div_phylo_treat1$S, treat1$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.012041 0.012041 0.9898    999  0.334
Residuals 21 0.255459 0.012165                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.339
Hot_dry   0.33111        

adonis2(formula=beta_div_phylo_treat1$S ~ Population, data=treat1[labels(beta_div_phylo_treat1$S),], permutations=999) %>%
        as.matrix() %>%
        kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	1	0.8963254	0.1888758	4.889993	0.001
Residual	21	3.8492558	0.8111242	NA	NA
Total	22	4.7455811	1.0000000	NA	NA

beta_richness_nmds_treat1 <- beta_div_richness_treat1$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(treat1_nmds, by = c("sample" = "Tube_code"))

beta_neutral_nmds_treat1 <- beta_div_neutral_treat1$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(treat1_nmds, by = c("sample" = "Tube_code"))

beta_phylo_nmds_treat1 <- beta_div_phylo_treat1$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(treat1_nmds, by = join_by(sample == Tube_code))

4.3.1.2 Acclimation vs antibiotics

treat <- meta  %>%
  filter(time_point == "1_Acclimation" | time_point == "2_Antibiotics")

temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL

treat.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(treat))]
identical(sort(colnames(treat.counts)),sort(as.character(rownames(treat))))

treat_nmds <- sample_metadata %>%
  filter(time_point == "1_Acclimation" | time_point == "2_Antibiotics")

4.3.1.2.1 Number of samples used

[1] 50

beta_div_richness_treat<-hillpair(data=treat.counts, q=0)
beta_div_neutral_treat<-hillpair(data=treat.counts, q=1)
beta_div_phylo_treat<-hillpair(data=treat.counts, q=1, tree=genome_tree)

#Arrange of metadata dataframe
treat_arrange<-treat[labels(beta_div_neutral_treat$S),]
treat_arrange$type_time <- interaction(treat_arrange$type, treat_arrange$time_point)

4.3.1.2.2 Richness

betadisper(beta_div_richness_treat$S, treat$time_point) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq     F N.Perm Pr(>F)  
Groups     1 0.025318 0.0253178 6.021    999  0.027 *
Residuals 48 0.201837 0.0042049                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              1_Acclimation 2_Antibiotics
1_Acclimation                       0.022
2_Antibiotics      0.017817              

adonis2(formula=beta_div_richness_treat$S ~ time_point*Population, data=treat[labels(beta_div_neutral_treat$S),], permutations=999,strata=treat$individual) %>%
        as.matrix() %>%
        kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	3	4.885035	0.2455889	4.991572	0.001
Residual	46	15.006068	0.7544111	NA	NA
Total	49	19.891103	1.0000000	NA	NA

pairwise <- pairwise.adonis(beta_div_richness_treat$S, treat_arrange$type_time, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control.1_Acclimation vs Treatment.1_Acclimation	1	0.3620815	1.0521088	0.06169963	0.322	1.000
Control.1_Acclimation vs Hot_control.1_Acclimation	1	1.2800877	4.6054436	0.22350616	0.001	0.015	.
Control.1_Acclimation vs Control.2_Antibiotics	1	1.0114074	2.8299035	0.15871670	0.001	0.015	.
Control.1_Acclimation vs Treatment.2_Antibiotics	1	0.9204347	2.4632144	0.14961929	0.004	0.060
Control.1_Acclimation vs Hot_control.2_Antibiotics	1	1.3738469	4.0051127	0.21073870	0.001	0.015	.
Treatment.1_Acclimation vs Hot_control.1_Acclimation	1	1.3606630	5.0871520	0.24124415	0.001	0.015	.
Treatment.1_Acclimation vs Control.2_Antibiotics	1	0.9707039	2.8037141	0.15747917	0.001	0.015	.
Treatment.1_Acclimation vs Treatment.2_Antibiotics	1	0.8277738	2.2885999	0.14050317	0.005	0.075
Treatment.1_Acclimation vs Hot_control.2_Antibiotics	1	1.4551542	4.3850542	0.22620799	0.001	0.015	.
Hot_control.1_Acclimation vs Control.2_Antibiotics	1	1.7346072	6.2936958	0.29556615	0.001	0.015	.
Hot_control.1_Acclimation vs Treatment.2_Antibiotics	1	1.5634787	5.4659371	0.28079496	0.001	0.015	.
Hot_control.1_Acclimation vs Hot_control.2_Antibiotics	1	1.2328439	4.7193158	0.23932452	0.001	0.015	.
Control.2_Antibiotics vs Treatment.2_Antibiotics	1	0.3007616	0.7949621	0.05762698	0.812	1.000
Control.2_Antibiotics vs Hot_control.2_Antibiotics	1	1.0978513	3.1806716	0.18513081	0.001	0.015	.
Treatment.2_Antibiotics vs Hot_control.2_Antibiotics	1	1.0695525	2.9566393	0.18529210	0.001	0.015	.

4.3.1.2.3 Neutral

betadisper(beta_div_neutral_treat$S, treat$time_point) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq  Mean Sq      F N.Perm Pr(>F)  
Groups     1 0.039587 0.039587 6.8387    999  0.016 *
Residuals 48 0.277854 0.005789                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              1_Acclimation 2_Antibiotics
1_Acclimation                       0.015
2_Antibiotics      0.011886              

adonis2(formula=beta_div_neutral_treat$S ~ time_point*Population, data=treat[labels(beta_div_neutral_treat$S),], permutations=999,strata=treat$individual) %>%
        as.matrix() %>%
        kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	3	5.756853	0.3024978	6.649871	0.001
Residual	46	13.274204	0.6975022	NA	NA
Total	49	19.031057	1.0000000	NA	NA

pairwise <- pairwise.adonis(beta_div_neutral_treat$S, treat_arrange$type_time, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control.1_Acclimation vs Treatment.1_Acclimation	1	0.2316020	0.7712905	0.04598874	0.727	1.000
Control.1_Acclimation vs Hot_control.1_Acclimation	1	1.4015347	5.7562378	0.26457873	0.001	0.015	.
Control.1_Acclimation vs Control.2_Antibiotics	1	1.0524088	3.2499157	0.17807839	0.003	0.045	.
Control.1_Acclimation vs Treatment.2_Antibiotics	1	1.1115532	3.4118917	0.19595181	0.001	0.015	.
Control.1_Acclimation vs Hot_control.2_Antibiotics	1	1.7659070	6.0767588	0.28831562	0.001	0.015	.
Treatment.1_Acclimation vs Hot_control.1_Acclimation	1	1.6347704	6.8326887	0.29925029	0.001	0.015	.
Treatment.1_Acclimation vs Control.2_Antibiotics	1	1.0258101	3.2124718	0.17638857	0.001	0.015	.
Treatment.1_Acclimation vs Treatment.2_Antibiotics	1	0.8812272	2.7455916	0.16395907	0.003	0.045	.
Treatment.1_Acclimation vs Hot_control.2_Antibiotics	1	1.8759542	6.5571073	0.30417380	0.001	0.015	.
Hot_control.1_Acclimation vs Control.2_Antibiotics	1	1.8018302	6.9639403	0.31706243	0.001	0.015	.
Hot_control.1_Acclimation vs Treatment.2_Antibiotics	1	1.7960588	7.0145016	0.33379338	0.001	0.015	.
Hot_control.1_Acclimation vs Hot_control.2_Antibiotics	1	1.1400941	5.0556070	0.25207948	0.001	0.015	.
Control.2_Antibiotics vs Treatment.2_Antibiotics	1	0.3090918	0.8838203	0.06365829	0.573	1.000
Control.2_Antibiotics vs Hot_control.2_Antibiotics	1	1.3183156	4.2483600	0.23280777	0.001	0.015	.
Treatment.2_Antibiotics vs Hot_control.2_Antibiotics	1	1.4966720	4.8065093	0.26992990	0.001	0.015	.

4.3.1.2.4 Phylogenetic

betadisper(beta_div_phylo_treat$S, treat$time_point) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq Mean Sq      F N.Perm Pr(>F)    
Groups     1 0.58372 0.58372 35.413    999  0.001 ***
Residuals 48 0.79119 0.01648                         
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              1_Acclimation 2_Antibiotics
1_Acclimation                       0.001
2_Antibiotics    2.9795e-07              

adonis2(formula=beta_div_phylo_treat$S ~ time_point*Population, data=treat[labels(beta_div_phylo_treat$S),], permutations=999,strata=treat$individual) %>%
        as.matrix() %>%
        kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	3	2.947011	0.344846	8.070832	0.001
Residual	46	5.598866	0.655154	NA	NA
Total	49	8.545877	1.000000	NA	NA

pairwise <- pairwise.adonis(beta_div_phylo_treat$S, treat_arrange$type_time, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control.1_Acclimation vs Treatment.1_Acclimation	1	0.04186923	0.4391642	0.02671451	0.743	1.000
Control.1_Acclimation vs Hot_control.1_Acclimation	1	0.15609416	2.5546889	0.13768428	0.037	0.555
Control.1_Acclimation vs Control.2_Antibiotics	1	0.86133708	5.6599878	0.27395891	0.002	0.030	.
Control.1_Acclimation vs Treatment.2_Antibiotics	1	0.91317327	6.5682162	0.31933815	0.001	0.015	.
Control.1_Acclimation vs Hot_control.2_Antibiotics	1	0.70006680	5.4923385	0.26801912	0.002	0.030	.
Treatment.1_Acclimation vs Hot_control.1_Acclimation	1	0.23108846	4.0521838	0.20208192	0.003	0.045	.
Treatment.1_Acclimation vs Control.2_Antibiotics	1	0.69687259	4.7138353	0.23911305	0.001	0.015	.
Treatment.1_Acclimation vs Treatment.2_Antibiotics	1	0.68913760	5.1284906	0.26810744	0.004	0.060
Treatment.1_Acclimation vs Hot_control.2_Antibiotics	1	0.59659295	4.8457011	0.24416880	0.002	0.030	.
Hot_control.1_Acclimation vs Control.2_Antibiotics	1	1.09724077	9.8570170	0.39654867	0.001	0.015	.
Hot_control.1_Acclimation vs Treatment.2_Antibiotics	1	1.22358928	12.8466332	0.47851934	0.001	0.015	.
Hot_control.1_Acclimation vs Hot_control.2_Antibiotics	1	0.66150542	7.6387977	0.33742064	0.001	0.015	.
Control.2_Antibiotics vs Treatment.2_Antibiotics	1	0.09408368	0.4635496	0.03442997	0.888	1.000
Control.2_Antibiotics vs Hot_control.2_Antibiotics	1	0.63116074	3.3932653	0.19509076	0.001	0.015	.
Treatment.2_Antibiotics vs Hot_control.2_Antibiotics	1	0.75130538	4.3068752	0.24885343	0.002	0.030	.

beta_richness_nmds_treat <- beta_div_richness_treat$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(treat_nmds, by = c("sample" = "Tube_code"))

beta_neutral_nmds_treat <- beta_div_neutral_treat$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(treat_nmds, by = c("sample" = "Tube_code"))

beta_phylo_nmds_treat <- beta_div_phylo_treat$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(treat_nmds, by = join_by(sample == Tube_code))

4.3.2 2. Does the FMT work?

4.3.2.1 Comparison between FMT1 and FMT2

#Create newID to identify duplicated samples
transplants_metadata<-sample_metadata%>%
  mutate(Tube_code=str_remove_all(Tube_code, "_a"))
transplants_metadata$newID <- paste(transplants_metadata$Tube_code, "_", transplants_metadata$individual)

transplant_all<-transplants_metadata%>%
  filter(time_point == "3_Transplant1" | time_point == "4_Transplant2")%>%
  filter(Tube_code != "AD45"| Tube_code != "AD48") %>%
  column_to_rownames("newID")

transplant_all_nmds <- transplants_metadata %>%
  filter(time_point == "3_Transplant1" | time_point == "4_Transplant2")%>%
  filter(Tube_code != "AD45"| Tube_code != "AD48")

full_counts<-temp_genome_counts %>%
    t()%>%
    as.data.frame()%>%
    rownames_to_column("Tube_code")%>%
    full_join(transplants_metadata,by = join_by(Tube_code == Tube_code))

transplant_all_counts<-full_counts %>%
  filter(time_point == "3_Transplant1" | time_point == "4_Transplant2") %>%
  subset(select=-c(315:324)) %>%
  column_to_rownames("newID")%>%
  subset(select=-c(1))%>%
  t() %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric) %>%
  subset(select=-c(47:48))

identical(sort(colnames(transplant_all_counts)),sort(as.character(rownames(transplant_all))))

4.3.2.2 Number of samples used

[1] 48

beta_div_richness_transplant_all<-hillpair(data=transplant_all_counts, q=0)
beta_div_neutral_transplant_all<-hillpair(data=transplant_all_counts, q=1)
beta_div_phylo_transplant_all<-hillpair(data=transplant_all_counts, q=1, tree=genome_tree)

#Arrange of metadata dataframe
transplant_all_arrange<-transplant_all[labels(beta_div_neutral_transplant_all$S),]
transplant_all_arrange$type_time <- interaction(transplant_all_arrange$type, transplant_all_arrange$time_point)

4.3.2.2.1 Richness

adonis2(formula=beta_div_richness_transplant_all$S ~ time_point*type, data=transplant_all[labels(beta_div_richness_transplant_all$S),], permutations=999) %>%
        as.matrix() %>%
        kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	5	3.636954	0.3294321	3.930187	0.001
Residual	40	7.403117	0.6705679	NA	NA
Total	45	11.040070	1.0000000	NA	NA

pairwise<-pairwise.adonis(beta_div_richness_transplant_all$S,transplant_all_arrange$type_time, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control.3_Transplant1 vs Treatment.3_Transplant1	1	1.129362662	5.79950801	0.292911723	0.001	0.015	.
Control.3_Transplant1 vs Hot_control.3_Transplant1	1	1.208865861	6.33395382	0.296895450	0.001	0.015	.
Control.3_Transplant1 vs Control.4_Transplant2	1	0.131647583	0.64704882	0.038868680	0.878	1.000
Control.3_Transplant1 vs Treatment.4_Transplant2	1	1.120646022	5.84730567	0.310246237	0.001	0.015	.
Control.3_Transplant1 vs Hot_control.4_Transplant2	1	1.223919777	6.45841493	0.315685010	0.001	0.015	.
Treatment.3_Transplant1 vs Hot_control.3_Transplant1	1	0.009102921	0.05153754	0.003948772	1.000	1.000
Treatment.3_Transplant1 vs Control.4_Transplant2	1	1.314300451	6.84362433	0.328331782	0.001	0.015	.
Treatment.3_Transplant1 vs Treatment.4_Transplant2	1	0.074927876	0.42820379	0.037469037	0.975	1.000
Treatment.3_Transplant1 vs Hot_control.4_Transplant2	1	0.077903563	0.44805710	0.035994139	0.955	1.000
Hot_control.3_Transplant1 vs Control.4_Transplant2	1	1.410186386	7.48716456	0.332952807	0.001	0.015	.
Hot_control.3_Transplant1 vs Treatment.4_Transplant2	1	0.077823551	0.45304555	0.036380301	0.965	1.000
Hot_control.3_Transplant1 vs Hot_control.4_Transplant2	1	0.067817527	0.39659721	0.029604324	0.977	1.000
Control.4_Transplant2 vs Treatment.4_Transplant2	1	1.248404869	6.61377735	0.337200593	0.001	0.015	.
Control.4_Transplant2 vs Hot_control.4_Transplant2	1	1.357472348	7.26616675	0.341677315	0.001	0.015	.
Treatment.4_Transplant2 vs Hot_control.4_Transplant2	1	0.012433593	0.07386441	0.006670157	1.000	1.000

4.3.2.2.2 Neutral

adonis2(formula=beta_div_neutral_transplant_all$S ~ time_point*type, data=transplant_all[labels(beta_div_neutral_transplant_all$S),], permutations=999) %>%
        as.matrix() %>%
        kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	5	3.888690	0.3472704	4.256223	0.001
Residual	40	7.309185	0.6527296	NA	NA
Total	45	11.197875	1.0000000	NA	NA

pairwise<-pairwise.adonis(beta_div_neutral_transplant_all$S,transplant_all_arrange$type_time, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control.3_Transplant1 vs Treatment.3_Transplant1	1	1.210492327	6.33244693	0.311445393	0.001	0.015	.
Control.3_Transplant1 vs Hot_control.3_Transplant1	1	1.329816668	7.07170798	0.320396953	0.001	0.015	.
Control.3_Transplant1 vs Control.4_Transplant2	1	0.095527891	0.51782131	0.031349250	0.904	1.000
Control.3_Transplant1 vs Treatment.4_Transplant2	1	1.202957055	6.54398303	0.334833643	0.001	0.015	.
Control.3_Transplant1 vs Hot_control.4_Transplant2	1	1.336430279	7.33775774	0.343886074	0.001	0.015	.
Treatment.3_Transplant1 vs Hot_control.3_Transplant1	1	0.009634139	0.05096559	0.003905121	1.000	1.000
Treatment.3_Transplant1 vs Control.4_Transplant2	1	1.400930696	7.57729789	0.351169916	0.001	0.015	.
Treatment.3_Transplant1 vs Treatment.4_Transplant2	1	0.070676505	0.38364693	0.033701584	0.902	1.000
Treatment.3_Transplant1 vs Hot_control.4_Transplant2	1	0.078300724	0.42972488	0.034572356	0.883	1.000
Hot_control.3_Transplant1 vs Control.4_Transplant2	1	1.529217161	8.39335845	0.358792367	0.001	0.015	.
Hot_control.3_Transplant1 vs Treatment.4_Transplant2	1	0.067325622	0.37214249	0.030079066	0.937	1.000
Hot_control.3_Transplant1 vs Hot_control.4_Transplant2	1	0.060700684	0.33852115	0.025379211	0.945	1.000
Control.4_Transplant2 vs Treatment.4_Transplant2	1	1.318966989	7.44875581	0.364264500	0.001	0.015	.
Control.4_Transplant2 vs Hot_control.4_Transplant2	1	1.461842335	8.31259383	0.372551658	0.001	0.015	.
Treatment.4_Transplant2 vs Hot_control.4_Transplant2	1	0.012314222	0.07128989	0.006439167	1.000	1.000

4.3.2.2.3 Phylogenetic

adonis2(formula=beta_div_phylo_transplant_all$S ~ time_point*type, data=transplant_all[labels(beta_div_phylo_transplant_all$S),], permutations=999) %>%
        as.matrix() %>%
        kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	5	0.3748882	0.2242933	2.313177	0.007
Residual	40	1.2965312	0.7757067	NA	NA
Total	45	1.6714194	1.0000000	NA	NA

pairwise<-pairwise.adonis(beta_div_phylo_transplant_all$S,transplant_all_arrange$type_time, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control.3_Transplant1 vs Treatment.3_Transplant1	1	0.098602035	2.40012240	0.146347835	0.055	0.825
Control.3_Transplant1 vs Hot_control.3_Transplant1	1	0.111980145	2.78142890	0.156423250	0.015	0.225
Control.3_Transplant1 vs Control.4_Transplant2	1	0.007928377	0.35768341	0.021866385	0.930	1.000
Control.3_Transplant1 vs Treatment.4_Transplant2	1	0.107460204	3.49439587	0.211853523	0.014	0.210
Control.3_Transplant1 vs Hot_control.4_Transplant2	1	0.123295950	4.08042827	0.225682058	0.002	0.030	.
Treatment.3_Transplant1 vs Hot_control.3_Transplant1	1	0.001916654	0.03838869	0.002944282	0.984	1.000
Treatment.3_Transplant1 vs Control.4_Transplant2	1	0.138394849	4.84607372	0.257139699	0.001	0.015	.
Treatment.3_Transplant1 vs Treatment.4_Transplant2	1	0.022693131	0.56103262	0.048527899	0.690	1.000
Treatment.3_Transplant1 vs Hot_control.4_Transplant2	1	0.016177953	0.41465150	0.033400172	0.726	1.000
Hot_control.3_Transplant1 vs Control.4_Transplant2	1	0.151529980	5.30364409	0.261216364	0.001	0.015	.
Hot_control.3_Transplant1 vs Treatment.4_Transplant2	1	0.018543667	0.46976985	0.037672696	0.698	1.000
Hot_control.3_Transplant1 vs Hot_control.4_Transplant2	1	0.011324236	0.29624361	0.022280248	0.834	1.000
Control.4_Transplant2 vs Treatment.4_Transplant2	1	0.123072115	7.12848761	0.354149191	0.003	0.045	.
Control.4_Transplant2 vs Hot_control.4_Transplant2	1	0.149801593	8.46696532	0.376862883	0.001	0.015	.
Treatment.4_Transplant2 vs Hot_control.4_Transplant2	1	0.001788609	0.06719083	0.006071173	0.985	1.000

beta_richness_nmds_transplant_all <- beta_div_richness_transplant_all$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(transplant_all_nmds, by = join_by(sample == newID))

beta_neutral_nmds_transplant_all <- beta_div_neutral_transplant_all$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(transplant_all_nmds, by = join_by(sample == newID))

beta_phylo_nmds_transplant_all <- beta_div_phylo_transplant_all$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(transplant_all_nmds, by = join_by(sample == newID))

p0<-beta_richness_nmds_transplant_all %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p1<-beta_neutral_nmds_transplant_all %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
                theme_classic() +
                theme(legend.position="none")
  
p2<-beta_phylo_nmds_transplant_all %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
                theme_classic() +
                theme(legend.position="none")

4.3.2.3 Comparison between FMT2 vs Post-FMT2

#Create newID to identify duplicated samples
transplants_metadata<-sample_metadata%>%
  mutate(Tube_code=str_remove_all(Tube_code, "_a"))
transplants_metadata$newID <- paste(transplants_metadata$Tube_code, "_", transplants_metadata$individual)

transplant3<-transplants_metadata%>%
  filter(time_point == "4_Transplant2" | time_point == "6_Post-FMT2")%>%
  column_to_rownames("newID")

transplant3_nmds <- transplants_metadata %>%
  filter(time_point == "4_Transplant2" | time_point == "6_Post-FMT2")

full_counts<-temp_genome_counts %>%
    t()%>%
    as.data.frame()%>%
    rownames_to_column("Tube_code")%>%
    full_join(transplants_metadata,by = join_by(Tube_code == Tube_code))

transplant3_counts<-full_counts %>%
  filter(time_point == "4_Transplant2" | time_point == "6_Post-FMT2") %>%
  subset(select=-c(315:324)) %>%
  column_to_rownames("newID")%>%
  subset(select=-c(1))%>%
  t() %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric)

identical(sort(colnames(transplant3_counts)),sort(as.character(rownames(transplant3))))

4.3.2.4 Number of samples used

[1] 49

beta_div_richness_transplant3<-hillpair(data=transplant3_counts, q=0)
beta_div_neutral_transplant3<-hillpair(data=transplant3_counts, q=1)
beta_div_phylo_transplant3<-hillpair(data=transplant3_counts, q=1, tree=genome_tree)

#Arrange of metadata dataframe
transplant3_arrange<-transplant3[labels(beta_div_neutral_transplant3$S),]

4.3.2.4.1 Richness

adonis2(formula=beta_div_richness_transplant3$S ~ Population+time_point+type, data=transplant3[labels(beta_div_richness_transplant3$S),], permutations=999, strata=transplant3$individual) %>%
        as.matrix() %>%
        kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	3	3.500812	0.2535872	5.096117	0.001
Residual	45	10.304350	0.7464128	NA	NA
Total	48	13.805162	1.0000000	NA	NA

pairwise<-pairwise.adonis(beta_div_richness_transplant3$S,transplant3_arrange$type, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control vs Treatment	1	1.4169018	5.739828	0.15622903	0.001	0.003	*
Control vs Hot_control	1	2.0940966	8.509112	0.21005427	0.001	0.003	*
Treatment vs Hot_control	1	0.3004618	1.265034	0.04179854	0.161	0.483

4.3.2.4.2 Neutral

adonis2(formula=beta_div_neutral_transplant3$S ~ Population+time_point+type, data=transplant3[labels(beta_div_neutral_transplant3$S),], permutations=999, strata=transplant3$individual) %>%
        as.matrix() %>%
        kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	3	4.128749	0.3031142	6.524331	0.001
Residual	45	9.492350	0.6968858	NA	NA
Total	48	13.621099	1.0000000	NA	NA

pairwise<-pairwise.adonis(beta_div_neutral_transplant3$S,transplant3_arrange$type, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control vs Treatment	1	1.8758788	8.282671	0.21084796	0.001	0.003	*
Control vs Hot_control	1	2.4396317	10.635546	0.24945256	0.001	0.003	*
Treatment vs Hot_control	1	0.3158428	1.394345	0.04587515	0.142	0.426

4.3.2.4.3 Phylogenetic

adonis2(formula=beta_div_phylo_transplant3$S ~ Population+time_point+type, data=transplant3[labels(beta_div_phylo_transplant3$S),], permutations=999, strata=transplant3$individual) %>%
        as.matrix() %>%
        kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	3	0.3971179	0.2701357	5.551766	0.001
Residual	45	1.0729504	0.7298643	NA	NA
Total	48	1.4700683	1.0000000	NA	NA

pairwise<-pairwise.adonis(beta_div_phylo_transplant3$S,transplant3_arrange$type, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control vs Treatment	1	0.14387705	5.735321	0.15612552	0.001	0.003	*
Control vs Hot_control	1	0.22715701	9.044894	0.22036587	0.001	0.003	*
Treatment vs Hot_control	1	0.04648319	1.704277	0.05550617	0.148	0.444

beta_richness_nmds_transplant3 <- beta_div_richness_transplant3$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(transplant3_nmds, by = join_by(sample == newID))

beta_neutral_nmds_transplant3 <- beta_div_neutral_transplant3$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(transplant3_nmds, by = join_by(sample == newID))

beta_phylo_nmds_transplant3 <- beta_div_phylo_transplant3$S %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(transplant3_nmds, by = join_by(sample == newID))

p0<-beta_richness_nmds_transplant3 %>%
            group_by(individual) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p1<-beta_neutral_nmds_transplant3 %>%
            group_by(individual) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
                theme_classic() +
                theme(legend.position="none")
  
p2<-beta_phylo_nmds_transplant3 %>%
            group_by(individual) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Phylogenetic beta diversity") +
                theme_classic() +
                theme(legend.position="none")

4.3.2.5 Comparison between the different experimental time points (Acclimation vs Transplant samples)

The estimated time for calculating the 5151 pairwise combinations is 65 seconds.

ggarrange( p1, p2, p3, ncol=3, nrow=1, common.legend = TRUE, legend="right")

4.3.2.6 Comparison of acclimation samples to transplant samples

transplant7<-transplants_metadata%>%
  filter(time_point == "4_Transplant2" | time_point == "1_Acclimation"| time_point == "3_Transplant1")%>%
  column_to_rownames("newID")

transplant7_nmds <- transplants_metadata %>%
  filter(time_point == "4_Transplant2" | time_point == "1_Acclimation"| time_point == "3_Transplant1")

transplant7_counts<-full_counts %>%
  filter(time_point == "4_Transplant2" | time_point == "1_Acclimation"| time_point == "3_Transplant1") %>%
  subset(select=-c(315:324)) %>%
  column_to_rownames("newID")%>%
  subset(select=-c(1))%>%
  t() %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric)

transplant7_counts <- transplant7_counts[, !names(transplant7_counts) %in% c("AD45 _ LI1_2nd_2", "AD48 _ LI1_2nd_6")]

identical(sort(colnames(transplant7_counts)),sort(as.character(rownames(transplant7))))

[1] TRUE

4.3.2.7 Number of samples used

[1] 73

beta_div_richness_transplant7<-hillpair(data=transplant7_counts, q=0)
beta_div_neutral_transplant7<-hillpair(data=transplant7_counts, q=1)
beta_div_phylo_transplant7<-hillpair(data=transplant7_counts, q=1, tree=genome_tree)

#Arrange of metadata dataframe
transplant7_arrange<-transplant7[labels(beta_div_neutral_transplant7$S),]
transplant7_arrange <- transplant7_arrange %>%
  mutate(time_point = recode(time_point,
                             "3_Transplant1" = "Transplant",
                             "4_Transplant2" = "Transplant"))

transplant7_arrange$type_time <- interaction(transplant7_arrange$type, transplant7_arrange$time_point)

4.3.2.7.1 Richness

adonis2(formula=beta_div_richness_transplant7$S ~ Population*time_point+type, data=transplant7[labels(beta_div_richness_transplant7$S),], permutations=999, strata=transplant7$individual) %>%
  as.matrix() %>%
  kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	6	5.309519	0.2518733	3.703392	0.001
Residual	66	15.770599	0.7481267	NA	NA
Total	72	21.080119	1.0000000	NA	NA

pairwise<-pairwise.adonis(beta_div_richness_transplant7$S,transplant7_arrange$type_time, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control.1_Acclimation vs Treatment.1_Acclimation	1	0.36208146	1.0521088	0.06169963	0.367	1.000
Control.1_Acclimation vs Hot_control.1_Acclimation	1	1.28008774	4.6054436	0.22350616	0.001	0.015	.
Control.1_Acclimation vs Control.Transplant	1	0.55038651	2.2107376	0.08124505	0.004	0.060
Control.1_Acclimation vs Treatment.Transplant	1	1.62289430	6.7106689	0.25123553	0.001	0.015	.
Control.1_Acclimation vs Hot_control.Transplant	1	1.73215888	7.4315069	0.25250175	0.001	0.015	.
Treatment.1_Acclimation vs Hot_control.1_Acclimation	1	1.36066298	5.0871520	0.24124415	0.001	0.015	.
Treatment.1_Acclimation vs Control.Transplant	1	0.52860586	2.1820402	0.08027507	0.001	0.015	.
Treatment.1_Acclimation vs Treatment.Transplant	1	1.76810026	7.5736721	0.27467042	0.001	0.015	.
Treatment.1_Acclimation vs Hot_control.Transplant	1	1.87790626	8.3291875	0.27462613	0.001	0.015	.
Hot_control.1_Acclimation vs Control.Transplant	1	1.75314247	8.7706781	0.25971282	0.001	0.015	.
Hot_control.1_Acclimation vs Treatment.Transplant	1	0.27700454	1.5346880	0.07126586	0.076	1.000
Hot_control.1_Acclimation vs Hot_control.Transplant	1	0.26448976	1.4916174	0.06349573	0.076	1.000
Control.Transplant vs Treatment.Transplant	1	2.30884687	12.4299510	0.30002331	0.001	0.015	.
Control.Transplant vs Hot_control.Transplant	1	2.50396161	13.6713271	0.30604256	0.001	0.015	.
Treatment.Transplant vs Hot_control.Transplant	1	0.01688622	0.1023282	0.00392027	0.999	1.000

4.3.2.7.2 Neutral

adonis2(formula=beta_div_neutral_transplant7$S ~ Population+time_point*type, data=transplant7[labels(beta_div_neutral_transplant7$S),], permutations=999, strata=transplant7$individual) %>%
  as.matrix() %>%
  kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	8	7.284378	0.3492417	4.293351	0.001
Residual	64	13.573319	0.6507583	NA	NA
Total	72	20.857698	1.0000000	NA	NA

pairwise<-pairwise.adonis(beta_div_neutral_transplant7$S,transplant7_arrange$type_time, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control.1_Acclimation vs Treatment.1_Acclimation	1	0.23160196	0.7712905	0.045988741	0.743	1.000
Control.1_Acclimation vs Hot_control.1_Acclimation	1	1.40153474	5.7562378	0.264578733	0.001	0.015	.
Control.1_Acclimation vs Control.Transplant	1	0.56111203	2.5583085	0.092832565	0.002	0.030	.
Control.1_Acclimation vs Treatment.Transplant	1	1.88709838	8.3257794	0.293929402	0.001	0.015	.
Control.1_Acclimation vs Hot_control.Transplant	1	2.02585000	9.2317432	0.295588471	0.001	0.015	.
Treatment.1_Acclimation vs Hot_control.1_Acclimation	1	1.63477039	6.8326887	0.299250291	0.001	0.015	.
Treatment.1_Acclimation vs Control.Transplant	1	0.61335323	2.8313912	0.101733730	0.001	0.015	.
Treatment.1_Acclimation vs Treatment.Transplant	1	2.10939140	9.4473664	0.320822116	0.001	0.015	.
Treatment.1_Acclimation vs Hot_control.Transplant	1	2.24827218	10.3907678	0.320794118	0.001	0.015	.
Hot_control.1_Acclimation vs Control.Transplant	1	1.87351542	10.3925002	0.293635661	0.001	0.015	.
Hot_control.1_Acclimation vs Treatment.Transplant	1	0.34276062	1.9273510	0.087897118	0.060	0.900
Hot_control.1_Acclimation vs Hot_control.Transplant	1	0.31638309	1.8072337	0.075911118	0.064	0.960
Control.Transplant vs Treatment.Transplant	1	2.48701901	14.0199769	0.325894571	0.001	0.015	.
Control.Transplant vs Hot_control.Transplant	1	2.75304261	15.6912860	0.336064549	0.001	0.015	.
Treatment.Transplant vs Hot_control.Transplant	1	0.01764676	0.1022118	0.003915827	0.997	1.000

4.3.2.7.3 Phylogenetic

adonis2(formula=beta_div_phylo_transplant7$S ~ Population+time_point+type, data=transplant7[labels(beta_div_phylo_transplant7$S),], permutations=999, strata=transplant7$individual) %>%
  as.matrix() %>%
  kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	4	0.7377029	0.1879202	3.933904	0.019
Residual	68	3.1879143	0.8120798	NA	NA
Total	72	3.9256172	1.0000000	NA	NA

pairwise<-pairwise.adonis(beta_div_phylo_transplant7$S,transplant7_arrange$type_time, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control.1_Acclimation vs Treatment.1_Acclimation	1	0.04186923	0.43916424	0.026714511	0.748	1.000
Control.1_Acclimation vs Hot_control.1_Acclimation	1	0.15609416	2.55468892	0.137684276	0.046	0.690
Control.1_Acclimation vs Control.Transplant	1	0.03888650	0.83961027	0.032493148	0.478	1.000
Control.1_Acclimation vs Treatment.Transplant	1	0.28946588	4.58406811	0.186464994	0.002	0.030	.
Control.1_Acclimation vs Hot_control.Transplant	1	0.31864880	5.37781508	0.196429666	0.001	0.015	.
Treatment.1_Acclimation vs Hot_control.1_Acclimation	1	0.23108846	4.05218385	0.202081922	0.004	0.060
Treatment.1_Acclimation vs Control.Transplant	1	0.11794420	2.69844074	0.097422117	0.045	0.675
Treatment.1_Acclimation vs Treatment.Transplant	1	0.37640156	6.28511923	0.239113210	0.001	0.015	.
Treatment.1_Acclimation vs Hot_control.Transplant	1	0.40433696	7.18306079	0.246138020	0.001	0.015	.
Hot_control.1_Acclimation vs Control.Transplant	1	0.11597038	5.32063275	0.175478948	0.001	0.015	.
Hot_control.1_Acclimation vs Treatment.Transplant	1	0.03673004	1.13023077	0.053488804	0.335	1.000
Hot_control.1_Acclimation vs Hot_control.Transplant	1	0.04097680	1.30539166	0.056012432	0.290	1.000
Control.Transplant vs Treatment.Transplant	1	0.21736741	7.59281199	0.207494630	0.001	0.015	.
Control.Transplant vs Hot_control.Transplant	1	0.25837791	9.19762187	0.228810100	0.001	0.015	.
Treatment.Transplant vs Hot_control.Transplant	1	0.00180330	0.04804393	0.001844435	0.966	1.000

beta_richness_nmds_transplant7 <- beta_div_richness_transplant7$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(transplant7_nmds, by = join_by(sample == newID))

beta_neutral_nmds_transplant7 <- beta_div_neutral_transplant7$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(transplant7_nmds, by = join_by(sample == newID))

beta_phylo_nmds_transplant7 <- beta_div_phylo_transplant7$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(transplant7_nmds, by = join_by(sample == newID))

p0<-beta_richness_nmds_transplant7 %>%
  group_by(type) %>%
  mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
  mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
  scale_color_manual(name="Type",
                     breaks=c("Control", "Hot_control", "Treatment"),
                     labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                     values=c("#4477AA","#d57d2c","#76b183")) +
  scale_shape_manual(name="time_point",
                     breaks=c("1_Acclimation", "3_Transplant1", "4_Transplant2"),
                     labels=c("Acclimation", "Transplant", "Transplant"),
                     values=c("circle","square","square")) +
  geom_point(size=2) +
  geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
  labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
  theme_classic() +
  theme(legend.position="none")

p1<-beta_neutral_nmds_transplant7 %>%
  group_by(type) %>%
  mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
  mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
  scale_color_manual(name="Type",
                     breaks=c("Control", "Hot_control", "Treatment"),
                     labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                     values=c("#4477AA","#d57d2c","#76b183")) +
  scale_shape_manual(name="time_point",
                     breaks=c("1_Acclimation", "3_Transplant1", "4_Transplant2"),
                     labels=c("Acclimation", "Transplant", "Transplant"),
                     values=c("circle","square","square")) +
  geom_point(size=2) +
  geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
  labs(y= element_blank (), x="NMDS1 \n Neutral beta diversity") +
  theme_classic() +
  theme(legend.position="none")

p2<-beta_phylo_nmds_transplant7 %>%
  group_by(type) %>%
  mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
  mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
  scale_color_manual(name="Type",
                     breaks=c("Control", "Hot_control", "Treatment"),
                     labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                     values=c("#4477AA","#d57d2c","#76b183")) +
  scale_shape_manual(name="time_point",
                     breaks=c("1_Acclimation", "3_Transplant1", "4_Transplant2"),
                     labels=c("Acclimation", "Transplant", "Transplant"),
                     values=c("circle","square","square")) +
  geom_point(size=2) +
  geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
  labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
  theme_classic() +
  theme(legend.position="none")

4.3.2.8 Comparison between Acclimation vs Post-FMT1

post3 <- meta  %>%
  filter(time_point == "1_Acclimation" | time_point == "5_Post-FMT1")

temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL

post3.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post3))]
identical(sort(colnames(post3.counts)),sort(as.character(rownames(post3))))

post3_nmds <- sample_metadata %>%
  filter(time_point == "1_Acclimation" | time_point == "5_Post-FMT1")

4.3.2.9 Number of samples used

[1] 53

beta_div_richness_post3<-hillpair(data=post3.counts, q=0)
beta_div_neutral_post3<-hillpair(data=post3.counts, q=1)
beta_div_phylo_post3<-hillpair(data=post3.counts, q=1, tree=genome_tree)

#Arrange of metadata dataframe
post3_arrange<-post3[labels(beta_div_neutral_post3$S),]
post3_arrange$type_time <- interaction(post3_arrange$type, post3_arrange$time_point)

4.3.2.9.1 Richness

betadisper(beta_div_richness_post3$S, post3_arrange$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq  Mean Sq      F N.Perm Pr(>F)    
Groups     2 0.099607 0.049803 9.5441    999  0.001 ***
Residuals 50 0.260911 0.005218                         
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
               Control Hot_control Treatment
Control                 0.00200000     0.891
Hot_control 0.00102653                 0.001
Treatment   0.88832670  0.00010131          

adonis2(formula=beta_div_richness_post3$S ~ time_point*Population, data=post3[labels(beta_div_neutral_post3$S),], permutations=999,strata=post3$individual) %>%
  as.matrix() %>%
  kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	3	3.479739	0.1872879	3.763983	0.001
Residual	49	15.099892	0.8127121	NA	NA
Total	52	18.579631	1.0000000	NA	NA

pairwise <- pairwise.adonis(beta_div_richness_post3$S, post3_arrange$type_time, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control.1_Acclimation vs Treatment.1_Acclimation	1	0.3620815	1.052109	0.06169963	0.293	1.000
Control.1_Acclimation vs Hot_control.1_Acclimation	1	1.2800877	4.605444	0.22350616	0.001	0.015	.
Control.1_Acclimation vs Control.5_Post-FMT1	1	0.6845657	1.998114	0.11101796	0.001	0.015	.
Control.1_Acclimation vs Treatment.5_Post-FMT1	1	0.8437461	2.499232	0.14281954	0.002	0.030	.
Control.1_Acclimation vs Hot_control.5_Post-FMT1	1	1.1208022	3.568670	0.18236649	0.002	0.030	.
Treatment.1_Acclimation vs Hot_control.1_Acclimation	1	1.3606630	5.087152	0.24124415	0.001	0.015	.
Treatment.1_Acclimation vs Control.5_Post-FMT1	1	0.7216200	2.172734	0.11956009	0.001	0.015	.
Treatment.1_Acclimation vs Treatment.5_Post-FMT1	1	0.9551308	2.926054	0.16322910	0.001	0.015	.
Treatment.1_Acclimation vs Hot_control.5_Post-FMT1	1	1.2263345	4.039487	0.20157637	0.001	0.015	.
Hot_control.1_Acclimation vs Control.5_Post-FMT1	1	1.4319792	5.384836	0.25180628	0.001	0.015	.
Hot_control.1_Acclimation vs Treatment.5_Post-FMT1	1	0.8172413	3.194690	0.17558364	0.001	0.015	.
Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1	1	0.5796135	2.441615	0.13239702	0.001	0.015	.
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1	1	0.5615418	1.729004	0.10335366	0.018	0.270
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1	1	0.8438429	2.793772	0.14865413	0.002	0.030	.
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1	1	0.3734921	1.268929	0.07799710	0.128	1.000

4.3.2.9.2 Neutral

betadisper(beta_div_neutral_post3$S, post3$time_point) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     1 0.00945 0.0094472 1.1428    999  0.288
Residuals 51 0.42161 0.0082669                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              1_Acclimation 5_Post-FMT1
1_Acclimation                     0.286
5_Post-FMT1          0.2901            

adonis2(formula=beta_div_neutral_post3$S ~ time_point*Population, data=post3[labels(beta_div_neutral_post3$S),], permutations=999,strata=post3$individual) %>%
  as.matrix() %>%
  kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	3	4.465574	0.2549304	5.588555	0.001
Residual	49	13.051264	0.7450696	NA	NA
Total	52	17.516838	1.0000000	NA	NA

pairwise <- pairwise.adonis(beta_div_neutral_post3$S, post3_arrange$type_time, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control.1_Acclimation vs Treatment.1_Acclimation	1	0.2316020	0.7712905	0.04598874	0.722	1.000
Control.1_Acclimation vs Hot_control.1_Acclimation	1	1.4015347	5.7562378	0.26457873	0.001	0.015	.
Control.1_Acclimation vs Control.5_Post-FMT1	1	0.8332162	2.9081103	0.15380227	0.001	0.015	.
Control.1_Acclimation vs Treatment.5_Post-FMT1	1	1.1719595	4.0685514	0.21336447	0.001	0.015	.
Control.1_Acclimation vs Hot_control.5_Post-FMT1	1	1.4260875	5.2413171	0.24675104	0.001	0.015	.
Treatment.1_Acclimation vs Hot_control.1_Acclimation	1	1.6347704	6.8326887	0.29925029	0.001	0.015	.
Treatment.1_Acclimation vs Control.5_Post-FMT1	1	0.9517634	3.3715700	0.17404733	0.001	0.015	.
Treatment.1_Acclimation vs Treatment.5_Post-FMT1	1	1.3127773	4.6298256	0.23585668	0.001	0.015	.
Treatment.1_Acclimation vs Hot_control.5_Post-FMT1	1	1.6713369	6.2395460	0.28056085	0.002	0.030	.
Hot_control.1_Acclimation vs Control.5_Post-FMT1	1	1.5409781	6.8338056	0.29928456	0.001	0.015	.
Hot_control.1_Acclimation vs Treatment.5_Post-FMT1	1	0.9133614	4.0964534	0.21451383	0.001	0.015	.
Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1	1	0.6954835	3.2951234	0.17077493	0.001	0.015	.
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1	1	0.6051778	2.2508491	0.13047758	0.009	0.135
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1	1	1.0528902	4.1436369	0.20570451	0.001	0.015	.
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1	1	0.4150076	1.6372683	0.09840968	0.041	0.615

4.3.2.9.3 Phylogenetic

betadisper(beta_div_phylo_post3$S, post3$time_point) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.05132 0.051320 2.6745    999   0.11
Residuals 51 0.97861 0.019189                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              1_Acclimation 5_Post-FMT1
1_Acclimation                     0.113
5_Post-FMT1         0.10812            

adonis2(formula=beta_div_phylo_post3$S ~ time_point*Population, data=post3[labels(beta_div_phylo_post3$S),], permutations=999,strata=post3$individual) %>%
  as.matrix() %>%
  kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	3	0.7332141	0.2105602	4.356444	0.001
Residual	49	2.7489923	0.7894398	NA	NA
Total	52	3.4822065	1.0000000	NA	NA

pairwise <- pairwise.adonis(beta_div_phylo_post3$S, post3_arrange$type_time, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control.1_Acclimation vs Treatment.1_Acclimation	1	0.04186923	0.4391642	0.02671451	0.726	1.000
Control.1_Acclimation vs Hot_control.1_Acclimation	1	0.15609416	2.5546889	0.13768428	0.039	0.585
Control.1_Acclimation vs Control.5_Post-FMT1	1	0.19193367	2.9749922	0.15678490	0.014	0.210
Control.1_Acclimation vs Treatment.5_Post-FMT1	1	0.14627288	1.7907381	0.10665035	0.157	1.000
Control.1_Acclimation vs Hot_control.5_Post-FMT1	1	0.25061348	3.6146185	0.18428187	0.007	0.105
Treatment.1_Acclimation vs Hot_control.1_Acclimation	1	0.23108846	4.0521838	0.20208192	0.003	0.045	.
Treatment.1_Acclimation vs Control.5_Post-FMT1	1	0.26358465	4.3608960	0.21417997	0.001	0.015	.
Treatment.1_Acclimation vs Treatment.5_Post-FMT1	1	0.25319427	3.2738422	0.17915456	0.046	0.690
Treatment.1_Acclimation vs Hot_control.5_Post-FMT1	1	0.39050120	5.9837393	0.27218933	0.002	0.030	.
Hot_control.1_Acclimation vs Control.5_Post-FMT1	1	0.14203376	5.4200212	0.25303529	0.001	0.015	.
Hot_control.1_Acclimation vs Treatment.5_Post-FMT1	1	0.09666753	2.3682173	0.13635351	0.029	0.435
Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1	1	0.09252600	2.9824958	0.15711821	0.007	0.105
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1	1	0.01842535	0.4144162	0.02688498	0.774	1.000
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1	1	0.05987967	1.7387847	0.09802164	0.113	1.000
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1	1	0.03212966	0.6477782	0.04139746	0.737	1.000

beta_richness_nmds_post3 <- beta_div_richness_post3$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(post3_nmds, by = c("sample" = "Tube_code"))

beta_neutral_nmds_post3 <- beta_div_neutral_post3$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(post3_nmds, by = c("sample" = "Tube_code"))

beta_phylo_nmds_post3 <- beta_div_phylo_post3$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(post3_nmds, by = join_by(sample == Tube_code))

4.3.2.10 Comparison between Acclimation vs Post-FMT2

post4 <- meta  %>%
  filter(time_point == "1_Acclimation" | time_point == "6_Post-FMT2")

temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL

post4.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post4))]
identical(sort(colnames(post4.counts)),sort(as.character(rownames(post4))))

post4_nmds <- sample_metadata %>%
  filter(time_point == "1_Acclimation" | time_point == "6_Post-FMT2")

4.3.2.11 Number of samples used

[1] 54

beta_div_richness_post4<-hillpair(data=post4.counts, q=0)
beta_div_neutral_post4<-hillpair(data=post4.counts, q=1)
beta_div_phylo_post4<-hillpair(data=post4.counts, q=1, tree=genome_tree)

#Arrange of metadata dataframe
post4_arrange<-post4[labels(beta_div_neutral_post4$S),]
post4_arrange$type_time <- interaction(post4_arrange$type, post4_arrange$time_point)

4.3.2.11.1 Richness

betadisper(beta_div_richness_post4$S, post4_arrange$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)  
Groups     2 0.06809 0.034047 3.8471    999  0.027 *
Residuals 51 0.45135 0.008850                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              Control Hot_control Treatment
Control                 0.0240000     0.884
Hot_control 0.0349385                 0.004
Treatment   0.8855174   0.0047257          

adonis2(formula=beta_div_richness_post4$S ~ time_point*Population, data=post4[labels(beta_div_richness_post4$S),], permutations=999,strata=post4$individual) %>%
  as.matrix() %>%
  kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	3	3.310172	0.1883377	3.867324	0.001
Residual	50	14.265560	0.8116623	NA	NA
Total	53	17.575732	1.0000000	NA	NA

pairwise <- pairwise.adonis(beta_div_richness_post4$S, post4_arrange$type_time, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control.1_Acclimation vs Treatment.1_Acclimation	1	0.3620815	1.052109	0.06169963	0.355	1.000
Control.1_Acclimation vs Hot_control.1_Acclimation	1	1.2800877	4.605444	0.22350616	0.001	0.015	.
Control.1_Acclimation vs Treatment.6_Post-FMT2	1	0.8430295	2.845779	0.15100353	0.001	0.015	.
Control.1_Acclimation vs Control.6_Post-FMT2	1	0.5232174	1.683240	0.09518843	0.027	0.405
Control.1_Acclimation vs Hot_control.6_Post-FMT2	1	1.1217138	3.634271	0.18509835	0.001	0.015	.
Treatment.1_Acclimation vs Hot_control.1_Acclimation	1	1.3606630	5.087152	0.24124415	0.001	0.015	.
Treatment.1_Acclimation vs Treatment.6_Post-FMT2	1	0.9130048	3.195028	0.16645080	0.001	0.015	.
Treatment.1_Acclimation vs Control.6_Post-FMT2	1	0.5959230	1.984036	0.11032208	0.001	0.015	.
Treatment.1_Acclimation vs Hot_control.6_Post-FMT2	1	1.2747787	4.275366	0.21086503	0.001	0.015	.
Hot_control.1_Acclimation vs Treatment.6_Post-FMT2	1	0.6397330	2.913695	0.15405213	0.001	0.015	.
Hot_control.1_Acclimation vs Control.6_Post-FMT2	1	1.4575447	6.224524	0.28007456	0.001	0.015	.
Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2	1	0.3276169	1.412318	0.08111028	0.041	0.615
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2	1	0.6463814	2.560441	0.13795154	0.002	0.030	.
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2	1	0.4796256	1.916520	0.10696943	0.002	0.030	.
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2	1	1.1305044	4.268317	0.21059061	0.001	0.015	.

4.3.2.11.2 Neutral

betadisper(beta_div_neutral_post4$S, post4_arrange$time_point) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     1 0.01544 0.0154447 2.0972    999  0.148
Residuals 52 0.38294 0.0073643                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              1_Acclimation 6_Post-FMT2
1_Acclimation                     0.142
6_Post-FMT2         0.15357            

adonis2(formula=beta_div_neutral_post4$S ~ time_point*Population, data=post4[labels(beta_div_neutral_post4$S),], permutations=999,strata=post4$individual) %>%
  as.matrix() %>%
  kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	3	3.863228	0.229321	4.959284	0.001
Residual	50	12.983151	0.770679	NA	NA
Total	53	16.846379	1.000000	NA	NA

pairwise <- pairwise.adonis(beta_div_neutral_post4$S, post4_arrange$type_time, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control.1_Acclimation vs Treatment.1_Acclimation	1	0.2316020	0.7712905	0.04598874	0.751	1.000
Control.1_Acclimation vs Hot_control.1_Acclimation	1	1.4015347	5.7562378	0.26457873	0.001	0.015	.
Control.1_Acclimation vs Treatment.6_Post-FMT2	1	1.1746426	4.5564741	0.22165640	0.001	0.015	.
Control.1_Acclimation vs Control.6_Post-FMT2	1	0.5286441	1.9819408	0.11021840	0.007	0.105
Control.1_Acclimation vs Hot_control.6_Post-FMT2	1	1.3443224	4.9104417	0.23483204	0.001	0.015	.
Treatment.1_Acclimation vs Hot_control.1_Acclimation	1	1.6347704	6.8326887	0.29925029	0.001	0.015	.
Treatment.1_Acclimation vs Treatment.6_Post-FMT2	1	1.3540292	5.3398081	0.25022756	0.001	0.015	.
Treatment.1_Acclimation vs Control.6_Post-FMT2	1	0.6311089	2.4041625	0.13063146	0.002	0.030	.
Treatment.1_Acclimation vs Hot_control.6_Post-FMT2	1	1.6125755	5.9825981	0.27215155	0.001	0.015	.
Hot_control.1_Acclimation vs Treatment.6_Post-FMT2	1	0.6202327	3.1519868	0.16457754	0.001	0.015	.
Hot_control.1_Acclimation vs Control.6_Post-FMT2	1	1.5701179	7.6327037	0.32297209	0.001	0.015	.
Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2	1	0.3634438	1.7083388	0.09647087	0.027	0.405
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2	1	1.0227481	4.6483346	0.22511910	0.001	0.015	.
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2	1	0.5010202	2.2065321	0.12119453	0.001	0.015	.
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2	1	1.3619424	5.7710313	0.26507845	0.001	0.015	.

4.3.2.11.3 Phylogenetic

betadisper(beta_div_phylo_post4$S, post4$time_point) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)  
Groups     1 0.06978 0.069777 5.0345    999  0.018 *
Residuals 52 0.72071 0.013860                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              1_Acclimation 6_Post-FMT2
1_Acclimation                     0.018
6_Post-FMT2        0.029131            

adonis2(formula=beta_div_phylo_post4$S ~ time_point*Population, data=post4[labels(beta_div_phylo_post4$S),], permutations=999,strata=post4$individual) %>%
  as.matrix() %>%
  kable()

	Df	SumOfSqs	R2	F	Pr(>F)
Model	3	0.757493	0.2376349	5.195124	0.001
Residual	50	2.430141	0.7623651	NA	NA
Total	53	3.187634	1.0000000	NA	NA

pairwise <- pairwise.adonis(beta_div_phylo_post4$S, post4_arrange$type_time, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control.1_Acclimation vs Treatment.1_Acclimation	1	0.04186923	0.4391642	0.02671451	0.728	1.000
Control.1_Acclimation vs Hot_control.1_Acclimation	1	0.15609416	2.5546889	0.13768428	0.034	0.510
Control.1_Acclimation vs Treatment.6_Post-FMT2	1	0.26322331	4.3060281	0.21205664	0.009	0.135
Control.1_Acclimation vs Control.6_Post-FMT2	1	0.16047895	2.5405742	0.13702781	0.049	0.735
Control.1_Acclimation vs Hot_control.6_Post-FMT2	1	0.25529510	4.0109138	0.20043631	0.002	0.030	.
Treatment.1_Acclimation vs Hot_control.1_Acclimation	1	0.23108846	4.0521838	0.20208192	0.001	0.015	.
Treatment.1_Acclimation vs Treatment.6_Post-FMT2	1	0.36496892	6.3966666	0.28560797	0.001	0.015	.
Treatment.1_Acclimation vs Control.6_Post-FMT2	1	0.22628210	3.8292220	0.19311005	0.016	0.240
Treatment.1_Acclimation vs Hot_control.6_Post-FMT2	1	0.34830814	5.8463335	0.26761166	0.002	0.030	.
Hot_control.1_Acclimation vs Treatment.6_Post-FMT2	1	0.10002871	4.3836237	0.21505615	0.001	0.015	.
Hot_control.1_Acclimation vs Control.6_Post-FMT2	1	0.12577510	5.0601287	0.24027055	0.001	0.015	.
Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2	1	0.06334378	2.4997737	0.13512455	0.017	0.255
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2	1	0.05927454	2.3820253	0.12958449	0.025	0.375
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2	1	0.06906280	2.7224602	0.14541146	0.006	0.090
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2	1	0.11081709	4.0436561	0.20174244	0.002	0.030	.

beta_richness_nmds_post4 <- beta_div_richness_post4$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(post4_nmds, by = c("sample" = "Tube_code"))

beta_neutral_nmds_post4 <- beta_div_neutral_post4$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(post4_nmds, by = c("sample" = "Tube_code"))

beta_phylo_nmds_post4 <- beta_div_phylo_post4$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(post4_nmds, by = join_by(sample == Tube_code))

4.3.2.12 All time comparison

all_comparison <- sample_metadata  %>%
  filter(time_point == "1_Acclimation" | time_point == "5_Post-FMT1"| time_point == "6_Post-FMT2"| time_point == "2_Antibiotics"| time_point == "3_Transplant1"| time_point == "4_Transplant2")%>%
  mutate(Tube_code=str_remove_all(Tube_code, "_a"))

all_comparison$newID <- paste(all_comparison$Tube_code, "_", all_comparison$individual)

all_comparison_data<-all_comparison%>%
  filter(Tube_code != "AD45_a") %>%
  filter(Tube_code != "AD48_a") %>%
  #mutate(newID=str_remove_all(newID, "_a")) %>%
  column_to_rownames("newID")

all_comparison_nmds <- sample_metadata %>%
  filter(time_point =="1_Acclimation" | time_point == "5_Post-FMT1"| time_point == "6_Post-FMT2"| time_point == "2_Antibiotics"| time_point == "3_Transplant1"| time_point == "4_Transplant2") %>%
  filter(Tube_code != "AD45_a"| Tube_code != "AD48_a")

all_comparison_nmds$newID <- paste(all_comparison_nmds$Tube_code, "_", all_comparison_nmds$individual)

full_counts_new<-temp_genome_counts %>%
    t()%>%
    as.data.frame()%>%
    rownames_to_column("Tube_code")%>%
    full_join(all_comparison,by = join_by(Tube_code == Tube_code))

comparison_all_counts<-full_counts_new %>%
  filter(time_point == "1_Acclimation" | time_point == "5_Post-FMT1"| time_point == "6_Post-FMT2"| time_point == "2_Antibiotics"| time_point == "3_Transplant1"| time_point == "4_Transplant2") %>%
  subset(select=-c(315:324)) %>%
  column_to_rownames("newID")%>%
  subset(select=-c(1))%>%
  t() %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric) %>%
  subset(select=-c(150:151))

identical(sort(colnames(comparison_all_counts)),sort(as.character(rownames(all_comparison))))

beta_div_richness_all_comparison<-hillpair(data=comparison_all_counts, q=0)
beta_div_neutral_all_comparison<-hillpair(data=comparison_all_counts, q=1)
beta_div_phylo_all_comparison<-hillpair(data=comparison_all_counts, q=1, tree=genome_tree)

#Arrange of metadata dataframe
all_comparison_arrange<-all_comparison_data[labels(beta_div_neutral_all_comparison$S),]
all_comparison_arrange$type_time <- interaction(all_comparison_arrange$type, all_comparison_arrange$time_point)

4.3.2.12.1 Richness

betadisper(beta_div_richness_all_comparison$S, all_comparison_arrange$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
           Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)  
Groups      2 0.09854 0.049271 3.9422    999  0.023 *
Residuals 146 1.82474 0.012498                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              Control Hot_control Treatment
Control                 0.0390000     0.554
Hot_control 0.0359859                 0.011
Treatment   0.5501317   0.0097545          

#(formula=beta_div_richness_all_comparison$S ~ time_point*type, data=all_comparison_data[labels(beta_div_richness_all_comparison$S),], permutations=999,strata=all_comparison_data$individual) %>%
  #as.matrix() %>%
  #kable()

pairwise <- pairwise.adonis(beta_div_richness_all_comparison$S, all_comparison_arrange$type_time, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control.1_Acclimation vs Treatment.1_Acclimation	1	0.362081458	1.05210877	0.061699628	0.338	1.000
Control.1_Acclimation vs Hot_control.1_Acclimation	1	1.280087737	4.60544357	0.223506160	0.001	0.153
Control.1_Acclimation vs Control.2_Antibiotics	1	1.011407380	2.82990350	0.158716703	0.002	0.306
Control.1_Acclimation vs Treatment.2_Antibiotics	1	0.920434738	2.46321443	0.149619289	0.006	0.918
Control.1_Acclimation vs Hot_control.2_Antibiotics	1	1.373846937	4.00511266	0.210738696	0.001	0.153
Control.1_Acclimation vs Control.3_Transplant1	1	0.488555082	1.74347500	0.098260065	0.008	1.000
Control.1_Acclimation vs Treatment.3_Transplant1	1	1.211627129	4.33073710	0.236255481	0.001	0.153
Control.1_Acclimation vs Hot_control.3_Transplant1	1	1.314649861	4.86501559	0.244903688	0.001	0.153
Control.1_Acclimation vs Control.4_Transplant2	1	0.402848480	1.44978255	0.083083130	0.072	1.000
Control.1_Acclimation vs Treatment.4_Transplant2	1	1.132799430	3.99953382	0.235273147	0.001	0.153
Control.1_Acclimation vs Hot_control.4_Transplant2	1	1.239570207	4.51495838	0.243854633	0.001	0.153
Control.1_Acclimation vs Control.5_Post-FMT1	1	0.684565686	1.99811396	0.111017964	0.001	0.153
Control.1_Acclimation vs Treatment.5_Post-FMT1	1	0.843746089	2.49923230	0.142819539	0.001	0.153
Control.1_Acclimation vs Hot_control.5_Post-FMT1	1	1.120802205	3.56866951	0.182366487	0.001	0.153
Control.1_Acclimation vs Treatment.6_Post-FMT2	1	0.843029506	2.84577913	0.151003527	0.001	0.153
Control.1_Acclimation vs Control.6_Post-FMT2	1	0.523217385	1.68323982	0.095188429	0.033	1.000
Control.1_Acclimation vs Hot_control.6_Post-FMT2	1	1.121713759	3.63427125	0.185098352	0.001	0.153
Treatment.1_Acclimation vs Hot_control.1_Acclimation	1	1.360662985	5.08715202	0.241244148	0.001	0.153
Treatment.1_Acclimation vs Control.2_Antibiotics	1	0.970703875	2.80371407	0.157479167	0.001	0.153
Treatment.1_Acclimation vs Treatment.2_Antibiotics	1	0.827773826	2.28859986	0.140503167	0.003	0.459
Treatment.1_Acclimation vs Hot_control.2_Antibiotics	1	1.455154159	4.38505425	0.226207995	0.001	0.153
Treatment.1_Acclimation vs Control.3_Transplant1	1	0.481272253	1.78421716	0.100325876	0.004	0.612
Treatment.1_Acclimation vs Treatment.3_Transplant1	1	1.312567757	4.90136959	0.259312933	0.001	0.153
Treatment.1_Acclimation vs Hot_control.3_Transplant1	1	1.417501836	5.47200891	0.267292230	0.001	0.153
Treatment.1_Acclimation vs Control.4_Transplant2	1	0.377460322	1.41165944	0.081075525	0.066	1.000
Treatment.1_Acclimation vs Treatment.4_Transplant2	1	1.238098167	4.57988771	0.260518599	0.002	0.306
Treatment.1_Acclimation vs Hot_control.4_Transplant2	1	1.348911044	5.13734533	0.268446080	0.001	0.153
Treatment.1_Acclimation vs Control.5_Post-FMT1	1	0.721620019	2.17273370	0.119560092	0.001	0.153
Treatment.1_Acclimation vs Treatment.5_Post-FMT1	1	0.955130770	2.92605353	0.163229097	0.001	0.153
Treatment.1_Acclimation vs Hot_control.5_Post-FMT1	1	1.226334542	4.03948698	0.201576367	0.001	0.153
Treatment.1_Acclimation vs Treatment.6_Post-FMT2	1	0.913004785	3.19502772	0.166450800	0.001	0.153
Treatment.1_Acclimation vs Control.6_Post-FMT2	1	0.595923023	1.98403628	0.110322079	0.001	0.153
Treatment.1_Acclimation vs Hot_control.6_Post-FMT2	1	1.274778685	4.27536566	0.210865033	0.001	0.153
Hot_control.1_Acclimation vs Control.2_Antibiotics	1	1.734607158	6.29369579	0.295566155	0.001	0.153
Hot_control.1_Acclimation vs Treatment.2_Antibiotics	1	1.563478726	5.46593706	0.280794962	0.001	0.153
Hot_control.1_Acclimation vs Hot_control.2_Antibiotics	1	1.232843872	4.71931579	0.239324520	0.001	0.153
Hot_control.1_Acclimation vs Control.3_Transplant1	1	1.296297619	6.36871987	0.284715438	0.001	0.153
Hot_control.1_Acclimation vs Treatment.3_Transplant1	1	0.220244257	1.14625733	0.075679246	0.283	1.000
Hot_control.1_Acclimation vs Hot_control.3_Transplant1	1	0.224204694	1.18982182	0.073491965	0.242	1.000
Hot_control.1_Acclimation vs Control.4_Transplant2	1	1.399239883	6.95480534	0.302978189	0.001	0.153
Hot_control.1_Acclimation vs Treatment.4_Transplant2	1	0.216302837	1.14530694	0.080967274	0.272	1.000
Hot_control.1_Acclimation vs Hot_control.4_Transplant2	1	0.196664515	1.05215668	0.069900726	0.364	1.000
Hot_control.1_Acclimation vs Control.5_Post-FMT1	1	1.431979200	5.38483613	0.251806284	0.001	0.153
Hot_control.1_Acclimation vs Treatment.5_Post-FMT1	1	0.817241324	3.19468982	0.175583637	0.001	0.153
Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1	1	0.579613496	2.44161478	0.132397016	0.001	0.153
Hot_control.1_Acclimation vs Treatment.6_Post-FMT2	1	0.639732962	2.91369490	0.154052125	0.001	0.153
Hot_control.1_Acclimation vs Control.6_Post-FMT2	1	1.457544655	6.22452356	0.280074556	0.001	0.153
Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2	1	0.327616900	1.41231806	0.081110284	0.037	1.000
Control.2_Antibiotics vs Treatment.2_Antibiotics	1	0.300761563	0.79496206	0.057626984	0.782	1.000
Control.2_Antibiotics vs Hot_control.2_Antibiotics	1	1.097851301	3.18067161	0.185130808	0.001	0.153
Control.2_Antibiotics vs Control.3_Transplant1	1	1.540776008	5.54176964	0.269780537	0.001	0.153
Control.2_Antibiotics vs Treatment.3_Transplant1	1	1.711923036	6.17547381	0.322050650	0.001	0.153
Control.2_Antibiotics vs Hot_control.3_Transplant1	1	1.841780800	6.89378453	0.329944272	0.001	0.153
Control.2_Antibiotics vs Control.4_Transplant2	1	1.511441382	5.48573893	0.267783308	0.001	0.153
Control.2_Antibiotics vs Treatment.4_Transplant2	1	1.612594527	5.74394061	0.323712795	0.001	0.153
Control.2_Antibiotics vs Hot_control.4_Transplant2	1	1.739395066	6.40461266	0.330056197	0.002	0.306
Control.2_Antibiotics vs Control.5_Post-FMT1	1	0.971238688	2.81865279	0.158185516	0.001	0.153
Control.2_Antibiotics vs Treatment.5_Post-FMT1	1	1.190641768	3.50854402	0.200390393	0.001	0.153
Control.2_Antibiotics vs Hot_control.5_Post-FMT1	1	1.551250242	4.93817626	0.247674421	0.001	0.153
Control.2_Antibiotics vs Treatment.6_Post-FMT2	1	1.490970612	5.05213442	0.251949958	0.001	0.153
Control.2_Antibiotics vs Control.6_Post-FMT2	1	1.198436711	3.85732325	0.204553064	0.001	0.153
Control.2_Antibiotics vs Hot_control.6_Post-FMT2	1	1.546155402	5.01421139	0.250532549	0.001	0.153
Treatment.2_Antibiotics vs Hot_control.2_Antibiotics	1	1.069552526	2.95663926	0.185292104	0.001	0.153
Treatment.2_Antibiotics vs Control.3_Transplant1	1	1.332989733	4.61829348	0.248051385	0.001	0.153
Treatment.2_Antibiotics vs Treatment.3_Transplant1	1	1.516514105	5.23811421	0.303868169	0.001	0.153
Treatment.2_Antibiotics vs Hot_control.3_Transplant1	1	1.623847037	5.84647092	0.310215687	0.002	0.306
Treatment.2_Antibiotics vs Control.4_Transplant2	1	1.316537672	4.60415208	0.247479813	0.001	0.153
Treatment.2_Antibiotics vs Treatment.4_Transplant2	1	1.438049740	4.88320766	0.307444678	0.001	0.153
Treatment.2_Antibiotics vs Hot_control.4_Transplant2	1	1.548346381	5.46312423	0.312837735	0.002	0.306
Treatment.2_Antibiotics vs Control.5_Post-FMT1	1	0.802328938	2.22911438	0.137352805	0.004	0.612
Treatment.2_Antibiotics vs Treatment.5_Post-FMT1	1	0.974454304	2.74115182	0.174139215	0.007	1.000
Treatment.2_Antibiotics vs Hot_control.5_Post-FMT1	1	1.304972399	3.98688766	0.221655227	0.001	0.153
Treatment.2_Antibiotics vs Treatment.6_Post-FMT2	1	1.278825644	4.16636433	0.229344973	0.001	0.153
Treatment.2_Antibiotics vs Control.6_Post-FMT2	1	1.017603829	3.14437063	0.183405428	0.001	0.153
Treatment.2_Antibiotics vs Hot_control.6_Post-FMT2	1	1.369140221	4.26359194	0.233447613	0.001	0.153
Hot_control.2_Antibiotics vs Control.3_Transplant1	1	1.825343641	6.92328189	0.315795871	0.002	0.306
Hot_control.2_Antibiotics vs Treatment.3_Transplant1	1	1.275008094	4.89212161	0.273423226	0.002	0.306
Hot_control.2_Antibiotics vs Hot_control.3_Transplant1	1	1.365719305	5.42464757	0.279266203	0.001	0.153
Hot_control.2_Antibiotics vs Control.4_Transplant2	1	1.871048444	7.16478012	0.323250674	0.001	0.153
Hot_control.2_Antibiotics vs Treatment.4_Transplant2	1	1.264776297	4.81313017	0.286272105	0.001	0.153
Hot_control.2_Antibiotics vs Hot_control.4_Transplant2	1	1.336062206	5.23953594	0.287262569	0.001	0.153
Hot_control.2_Antibiotics vs Control.5_Post-FMT1	1	1.256421540	3.80504531	0.202341725	0.001	0.153
Hot_control.2_Antibiotics vs Treatment.5_Post-FMT1	1	1.042883208	3.21925840	0.186956855	0.001	0.153
Hot_control.2_Antibiotics vs Hot_control.5_Post-FMT1	1	1.118057885	3.72987362	0.199140352	0.001	0.153
Hot_control.2_Antibiotics vs Treatment.6_Post-FMT2	1	1.393703594	4.96438715	0.248662136	0.001	0.153
Hot_control.2_Antibiotics vs Control.6_Post-FMT2	1	1.578393989	5.32675023	0.262056166	0.001	0.153
Hot_control.2_Antibiotics vs Hot_control.6_Post-FMT2	1	1.187086833	4.03801261	0.212102633	0.002	0.306
Control.3_Transplant1 vs Treatment.3_Transplant1	1	1.129362662	5.79950801	0.292911723	0.001	0.153
Control.3_Transplant1 vs Hot_control.3_Transplant1	1	1.208865861	6.33395382	0.296895450	0.001	0.153
Control.3_Transplant1 vs Control.4_Transplant2	1	0.131647583	0.64704882	0.038868680	0.884	1.000
Control.3_Transplant1 vs Treatment.4_Transplant2	1	1.120646022	5.84730567	0.310246237	0.001	0.153
Control.3_Transplant1 vs Hot_control.4_Transplant2	1	1.223919777	6.45841493	0.315685010	0.001	0.153
Control.3_Transplant1 vs Control.5_Post-FMT1	1	0.859252354	3.20381975	0.166832422	0.001	0.153
Control.3_Transplant1 vs Treatment.5_Post-FMT1	1	1.027552599	3.97918760	0.209660586	0.001	0.153
Control.3_Transplant1 vs Hot_control.5_Post-FMT1	1	1.201969568	5.01536301	0.238652219	0.001	0.153
Control.3_Transplant1 vs Treatment.6_Post-FMT2	1	0.813822425	3.66869509	0.186524580	0.001	0.153
Control.3_Transplant1 vs Control.6_Post-FMT2	1	0.635206288	2.68665949	0.143774198	0.001	0.153
Control.3_Transplant1 vs Hot_control.6_Post-FMT2	1	1.251952139	5.34475979	0.250401496	0.001	0.153
Treatment.3_Transplant1 vs Hot_control.3_Transplant1	1	0.009102921	0.05153754	0.003948772	1.000	1.000
Treatment.3_Transplant1 vs Control.4_Transplant2	1	1.314300451	6.84362433	0.328331782	0.001	0.153
Treatment.3_Transplant1 vs Treatment.4_Transplant2	1	0.074927876	0.42820379	0.037469037	0.974	1.000
Treatment.3_Transplant1 vs Hot_control.4_Transplant2	1	0.077903563	0.44805710	0.035994139	0.964	1.000
Treatment.3_Transplant1 vs Control.5_Post-FMT1	1	1.353702886	5.08846981	0.266572955	0.001	0.153
Treatment.3_Transplant1 vs Treatment.5_Post-FMT1	1	0.791197505	3.11042526	0.193069097	0.002	0.306
Treatment.3_Transplant1 vs Hot_control.5_Post-FMT1	1	0.536176861	2.29706971	0.140949861	0.003	0.459
Treatment.3_Transplant1 vs Treatment.6_Post-FMT2	1	0.621029781	2.91505641	0.172335010	0.001	0.153
Treatment.3_Transplant1 vs Control.6_Post-FMT2	1	1.331819749	5.79735453	0.292834809	0.002	0.306
Treatment.3_Transplant1 vs Hot_control.6_Post-FMT2	1	0.417936103	1.83930234	0.116122686	0.005	0.765
Hot_control.3_Transplant1 vs Control.4_Transplant2	1	1.410186386	7.48716456	0.332952807	0.001	0.153
Hot_control.3_Transplant1 vs Treatment.4_Transplant2	1	0.077823551	0.45304555	0.036380301	0.959	1.000
Hot_control.3_Transplant1 vs Hot_control.4_Transplant2	1	0.067817527	0.39659721	0.029604324	0.982	1.000
Hot_control.3_Transplant1 vs Control.5_Post-FMT1	1	1.469262870	5.70807523	0.275644895	0.001	0.153
Hot_control.3_Transplant1 vs Treatment.5_Post-FMT1	1	0.865197810	3.51773013	0.200809700	0.001	0.153
Hot_control.3_Transplant1 vs Hot_control.5_Post-FMT1	1	0.610686942	2.69073097	0.152098349	0.002	0.306
Hot_control.3_Transplant1 vs Treatment.6_Post-FMT2	1	0.678638979	3.26359386	0.178693957	0.001	0.153
Hot_control.3_Transplant1 vs Control.6_Post-FMT2	1	1.439165056	6.43874075	0.300332040	0.001	0.153
Hot_control.3_Transplant1 vs Hot_control.6_Post-FMT2	1	0.458703763	2.07389437	0.121465808	0.002	0.306
Control.4_Transplant2 vs Treatment.4_Transplant2	1	1.248404869	6.61377735	0.337200593	0.002	0.306
Control.4_Transplant2 vs Hot_control.4_Transplant2	1	1.357472348	7.26616675	0.341677315	0.001	0.153
Control.4_Transplant2 vs Control.5_Post-FMT1	1	0.833650830	3.13584966	0.163873030	0.001	0.153
Control.4_Transplant2 vs Treatment.5_Post-FMT1	1	1.080414607	4.22492316	0.219762811	0.001	0.153
Control.4_Transplant2 vs Hot_control.5_Post-FMT1	1	1.341135755	5.65148950	0.261020818	0.001	0.153
Control.4_Transplant2 vs Treatment.6_Post-FMT2	1	0.926258626	4.22028175	0.208715279	0.001	0.153
Control.4_Transplant2 vs Control.6_Post-FMT2	1	0.574654578	2.45496178	0.133024484	0.001	0.153
Control.4_Transplant2 vs Hot_control.6_Post-FMT2	1	1.352206080	5.83128366	0.267106770	0.001	0.153
Treatment.4_Transplant2 vs Hot_control.4_Transplant2	1	0.012433593	0.07386441	0.006670157	1.000	1.000
Treatment.4_Transplant2 vs Control.5_Post-FMT1	1	1.291262283	4.81032755	0.270086417	0.001	0.153
Treatment.4_Transplant2 vs Treatment.5_Post-FMT1	1	0.771548746	3.01386503	0.200738786	0.005	0.765
Treatment.4_Transplant2 vs Hot_control.5_Post-FMT1	1	0.598950488	2.56717762	0.164909637	0.005	0.765
Treatment.4_Transplant2 vs Treatment.6_Post-FMT2	1	0.584788938	2.76668773	0.175476788	0.001	0.153
Treatment.4_Transplant2 vs Control.6_Post-FMT2	1	1.258012524	5.48540220	0.296742378	0.001	0.153
Treatment.4_Transplant2 vs Hot_control.6_Post-FMT2	1	0.432278420	1.90731713	0.127945029	0.002	0.306
Hot_control.4_Transplant2 vs Control.5_Post-FMT1	1	1.399019323	5.36419518	0.277016170	0.001	0.153
Hot_control.4_Transplant2 vs Treatment.5_Post-FMT1	1	0.829245656	3.33377055	0.204102938	0.003	0.459
Hot_control.4_Transplant2 vs Hot_control.5_Post-FMT1	1	0.652357925	2.85882027	0.169574159	0.002	0.306
Hot_control.4_Transplant2 vs Treatment.6_Post-FMT2	1	0.631349786	3.03802796	0.178308661	0.002	0.306
Hot_control.4_Transplant2 vs Control.6_Post-FMT2	1	1.377318091	6.13498096	0.304692662	0.001	0.153
Hot_control.4_Transplant2 vs Hot_control.6_Post-FMT2	1	0.447315329	2.01494401	0.125816488	0.002	0.306
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1	1	0.561541773	1.72900374	0.103353658	0.014	1.000
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1	1	0.843842909	2.79377183	0.148654132	0.001	0.153
Control.5_Post-FMT1 vs Treatment.6_Post-FMT2	1	0.762813539	2.68392536	0.143648902	0.001	0.153
Control.5_Post-FMT1 vs Control.6_Post-FMT2	1	0.343260479	1.14873316	0.066986474	0.248	1.000
Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2	1	1.126958011	3.79925624	0.191888836	0.001	0.153
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1	1	0.373492058	1.26892925	0.077997097	0.123	1.000
Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2	1	0.357139663	1.29718436	0.079595612	0.135	1.000
Treatment.5_Post-FMT1 vs Control.6_Post-FMT2	1	0.776946658	2.67089786	0.151146698	0.001	0.153
Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2	1	0.650235954	2.25340711	0.130606500	0.001	0.153
Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2	1	0.413209131	1.61613759	0.091741881	0.006	0.918
Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2	1	1.016399223	3.76057141	0.190306815	0.002	0.306
Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2	1	0.273256319	1.01928120	0.059889791	0.438	1.000
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2	1	0.646381353	2.56044149	0.137951540	0.001	0.153
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2	1	0.479625583	1.91651999	0.106969433	0.003	0.459
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2	1	1.130504372	4.26831718	0.210590605	0.001	0.153

4.3.2.12.2 Neutral

betadisper(beta_div_neutral_all_comparison$S, all_comparison_arrange$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
           Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)  
Groups      2 0.11629 0.058145 4.2503    999  0.015 *
Residuals 146 1.99732 0.013680                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              Control Hot_control Treatment
Control                 0.1100000     0.193
Hot_control 0.0992801                 0.005
Treatment   0.1988089   0.0039096          

#adonis2(formula=beta_div_neutral_all_comparison$S ~ time_point*type, data=all_comparison_arrange[labels(beta_div_neutral_all_comparison$S),], permutations=999,strata=all_comparison_arrange$individual) %>%
  #as.matrix() %>%
  #kable()

pairwise <- pairwise.adonis(beta_div_neutral_all_comparison$S, all_comparison_arrange$type_time, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control.1_Acclimation vs Treatment.1_Acclimation	1	0.231601965	0.77129054	0.045988741	0.715	1.000
Control.1_Acclimation vs Hot_control.1_Acclimation	1	1.401534736	5.75623784	0.264578733	0.001	0.153
Control.1_Acclimation vs Control.2_Antibiotics	1	1.052408777	3.24991568	0.178078394	0.001	0.153
Control.1_Acclimation vs Treatment.2_Antibiotics	1	1.111553177	3.41189169	0.195951810	0.002	0.306
Control.1_Acclimation vs Hot_control.2_Antibiotics	1	1.765907003	6.07675876	0.288315619	0.001	0.153
Control.1_Acclimation vs Control.3_Transplant1	1	0.537039622	2.17218034	0.119533281	0.005	0.765
Control.1_Acclimation vs Treatment.3_Transplant1	1	1.405564870	5.47754050	0.281223417	0.001	0.153
Control.1_Acclimation vs Hot_control.3_Transplant1	1	1.533003108	6.15337104	0.290893164	0.001	0.153
Control.1_Acclimation vs Control.4_Transplant2	1	0.352392363	1.45768829	0.083498357	0.079	1.000
Control.1_Acclimation vs Treatment.4_Transplant2	1	1.310880994	5.15468039	0.283931211	0.001	0.153
Control.1_Acclimation vs Hot_control.4_Transplant2	1	1.444103332	5.83290868	0.294102533	0.001	0.153
Control.1_Acclimation vs Control.5_Post-FMT1	1	0.833216183	2.90811026	0.153802269	0.001	0.153
Control.1_Acclimation vs Treatment.5_Post-FMT1	1	1.171959521	4.06855143	0.213364473	0.001	0.153
Control.1_Acclimation vs Hot_control.5_Post-FMT1	1	1.426087479	5.24131714	0.246751042	0.001	0.153
Control.1_Acclimation vs Treatment.6_Post-FMT2	1	1.174642637	4.55647410	0.221656402	0.001	0.153
Control.1_Acclimation vs Control.6_Post-FMT2	1	0.528644071	1.98194080	0.110218403	0.002	0.306
Control.1_Acclimation vs Hot_control.6_Post-FMT2	1	1.344322430	4.91044173	0.234832042	0.001	0.153
Treatment.1_Acclimation vs Hot_control.1_Acclimation	1	1.634770392	6.83268874	0.299250291	0.001	0.153
Treatment.1_Acclimation vs Control.2_Antibiotics	1	1.025810137	3.21247184	0.176388569	0.001	0.153
Treatment.1_Acclimation vs Treatment.2_Antibiotics	1	0.881227194	2.74559155	0.163959066	0.001	0.153
Treatment.1_Acclimation vs Hot_control.2_Antibiotics	1	1.875954242	6.55710727	0.304173802	0.001	0.153
Treatment.1_Acclimation vs Control.3_Transplant1	1	0.566432465	2.33088738	0.127156276	0.005	0.765
Treatment.1_Acclimation vs Treatment.3_Transplant1	1	1.572048670	6.24379035	0.308429906	0.002	0.306
Treatment.1_Acclimation vs Hot_control.3_Transplant1	1	1.701844881	6.95690050	0.316843468	0.001	0.153
Treatment.1_Acclimation vs Control.4_Transplant2	1	0.401361323	1.68977427	0.095522659	0.045	1.000
Treatment.1_Acclimation vs Treatment.4_Transplant2	1	1.459328039	5.85815805	0.310643173	0.001	0.153
Treatment.1_Acclimation vs Hot_control.4_Transplant2	1	1.598342225	6.58427533	0.319869183	0.001	0.153
Treatment.1_Acclimation vs Control.5_Post-FMT1	1	0.951763439	3.37156998	0.174047327	0.001	0.153
Treatment.1_Acclimation vs Treatment.5_Post-FMT1	1	1.312777350	4.62982556	0.235856684	0.001	0.153
Treatment.1_Acclimation vs Hot_control.5_Post-FMT1	1	1.671336875	6.23954604	0.280560854	0.001	0.153
Treatment.1_Acclimation vs Treatment.6_Post-FMT2	1	1.354029183	5.33980806	0.250227558	0.001	0.153
Treatment.1_Acclimation vs Control.6_Post-FMT2	1	0.631108857	2.40416255	0.130631456	0.003	0.459
Treatment.1_Acclimation vs Hot_control.6_Post-FMT2	1	1.612575528	5.98259806	0.272151547	0.001	0.153
Hot_control.1_Acclimation vs Control.2_Antibiotics	1	1.801830169	6.96394029	0.317062430	0.001	0.153
Hot_control.1_Acclimation vs Treatment.2_Antibiotics	1	1.796058823	7.01450160	0.333793384	0.001	0.153
Hot_control.1_Acclimation vs Hot_control.2_Antibiotics	1	1.140094120	5.05560699	0.252079481	0.001	0.153
Hot_control.1_Acclimation vs Control.3_Transplant1	1	1.409800825	7.57086193	0.321195803	0.001	0.153
Hot_control.1_Acclimation vs Treatment.3_Transplant1	1	0.268029239	1.43433504	0.092931444	0.125	1.000
Hot_control.1_Acclimation vs Hot_control.3_Transplant1	1	0.271118909	1.47312872	0.089426165	0.105	1.000
Hot_control.1_Acclimation vs Control.4_Transplant2	1	1.448236257	8.01342873	0.333706145	0.001	0.153
Hot_control.1_Acclimation vs Treatment.4_Transplant2	1	0.259223626	1.44651346	0.100128897	0.133	1.000
Hot_control.1_Acclimation vs Hot_control.4_Transplant2	1	0.220654933	1.24074949	0.081410005	0.232	1.000
Hot_control.1_Acclimation vs Control.5_Post-FMT1	1	1.540978110	6.83380557	0.299284565	0.002	0.306
Hot_control.1_Acclimation vs Treatment.5_Post-FMT1	1	0.913361414	4.09645344	0.214513834	0.001	0.153
Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1	1	0.695483464	3.29512337	0.170774932	0.001	0.153
Hot_control.1_Acclimation vs Treatment.6_Post-FMT2	1	0.620232728	3.15198682	0.164577537	0.001	0.153
Hot_control.1_Acclimation vs Control.6_Post-FMT2	1	1.570117896	7.63270374	0.322972091	0.001	0.153
Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2	1	0.363443763	1.70833881	0.096470867	0.035	1.000
Control.2_Antibiotics vs Treatment.2_Antibiotics	1	0.309091831	0.88382033	0.063658295	0.599	1.000
Control.2_Antibiotics vs Hot_control.2_Antibiotics	1	1.318315639	4.24836003	0.232807771	0.001	0.153
Control.2_Antibiotics vs Control.3_Transplant1	1	1.689711387	6.43107429	0.300081751	0.001	0.153
Control.2_Antibiotics vs Treatment.3_Transplant1	1	1.689195930	6.13766780	0.320711377	0.001	0.153
Control.2_Antibiotics vs Hot_control.3_Transplant1	1	1.809255022	6.80473556	0.327076282	0.001	0.153
Control.2_Antibiotics vs Control.4_Transplant2	1	1.677507510	6.53011334	0.303301392	0.001	0.153
Control.2_Antibiotics vs Treatment.4_Transplant2	1	1.627593397	5.93402835	0.330880951	0.002	0.306
Control.2_Antibiotics vs Hot_control.4_Transplant2	1	1.753895650	6.60608411	0.336940517	0.001	0.153
Control.2_Antibiotics vs Control.5_Post-FMT1	1	1.193795915	3.91871436	0.207134284	0.001	0.153
Control.2_Antibiotics vs Treatment.5_Post-FMT1	1	1.408395455	4.57891410	0.246457574	0.001	0.153
Control.2_Antibiotics vs Hot_control.5_Post-FMT1	1	1.733940673	5.99463357	0.285531707	0.001	0.153
Control.2_Antibiotics vs Treatment.6_Post-FMT2	1	1.520214567	5.54808874	0.270005099	0.001	0.153
Control.2_Antibiotics vs Control.6_Post-FMT2	1	1.242673544	4.38276233	0.226116498	0.001	0.153
Control.2_Antibiotics vs Hot_control.6_Post-FMT2	1	1.690913277	5.80983211	0.279186880	0.002	0.306
Treatment.2_Antibiotics vs Hot_control.2_Antibiotics	1	1.496671964	4.80650925	0.269929899	0.002	0.306
Treatment.2_Antibiotics vs Control.3_Transplant1	1	1.585917342	6.09171700	0.303195441	0.001	0.153
Treatment.2_Antibiotics vs Treatment.3_Transplant1	1	1.638410357	5.99150564	0.333018579	0.001	0.153
Treatment.2_Antibiotics vs Hot_control.3_Transplant1	1	1.752495870	6.64990939	0.338419341	0.001	0.153
Treatment.2_Antibiotics vs Control.4_Transplant2	1	1.594532431	6.27600416	0.309528648	0.001	0.153
Treatment.2_Antibiotics vs Treatment.4_Transplant2	1	1.569536480	5.76455490	0.343853740	0.001	0.153
Treatment.2_Antibiotics vs Hot_control.4_Transplant2	1	1.692716606	6.43803058	0.349171271	0.001	0.153
Treatment.2_Antibiotics vs Control.5_Post-FMT1	1	1.203693742	3.94355452	0.219775548	0.001	0.153
Treatment.2_Antibiotics vs Treatment.5_Post-FMT1	1	1.342345723	4.35196799	0.250805441	0.001	0.153
Treatment.2_Antibiotics vs Hot_control.5_Post-FMT1	1	1.646821658	5.70346727	0.289465158	0.002	0.306
Treatment.2_Antibiotics vs Treatment.6_Post-FMT2	1	1.526094538	5.60220112	0.285794492	0.001	0.153
Treatment.2_Antibiotics vs Control.6_Post-FMT2	1	1.228569941	4.34707035	0.236935394	0.002	0.306
Treatment.2_Antibiotics vs Hot_control.6_Post-FMT2	1	1.612642625	5.54814849	0.283819641	0.002	0.306
Hot_control.2_Antibiotics vs Control.3_Transplant1	1	2.115607623	9.21771686	0.380618739	0.001	0.153
Hot_control.2_Antibiotics vs Treatment.3_Transplant1	1	1.101992427	4.65211649	0.263544403	0.001	0.153
Hot_control.2_Antibiotics vs Hot_control.3_Transplant1	1	1.133256122	4.92116448	0.260087823	0.002	0.306
Hot_control.2_Antibiotics vs Control.4_Transplant2	1	2.176300247	9.73032474	0.393457217	0.001	0.153
Hot_control.2_Antibiotics vs Treatment.4_Transplant2	1	1.172716857	5.03855938	0.295715105	0.001	0.153
Hot_control.2_Antibiotics vs Hot_control.4_Transplant2	1	1.204981537	5.30457458	0.289795021	0.001	0.153
Hot_control.2_Antibiotics vs Control.5_Post-FMT1	1	1.680870091	6.19302659	0.292220017	0.001	0.153
Hot_control.2_Antibiotics vs Treatment.5_Post-FMT1	1	1.405394667	5.16721122	0.269585969	0.001	0.153
Hot_control.2_Antibiotics vs Hot_control.5_Post-FMT1	1	1.362143588	5.32040618	0.261825779	0.001	0.153
Hot_control.2_Antibiotics vs Treatment.6_Post-FMT2	1	1.579851347	6.56137424	0.304311505	0.001	0.153
Hot_control.2_Antibiotics vs Control.6_Post-FMT2	1	1.877053661	7.49890802	0.333300977	0.002	0.306
Hot_control.2_Antibiotics vs Hot_control.6_Post-FMT2	1	1.274827637	4.94469852	0.247920444	0.001	0.153
Control.3_Transplant1 vs Treatment.3_Transplant1	1	1.210492327	6.33244693	0.311445393	0.001	0.153
Control.3_Transplant1 vs Hot_control.3_Transplant1	1	1.329816668	7.07170798	0.320396953	0.001	0.153
Control.3_Transplant1 vs Control.4_Transplant2	1	0.095527891	0.51782131	0.031349250	0.901	1.000
Control.3_Transplant1 vs Treatment.4_Transplant2	1	1.202957055	6.54398303	0.334833643	0.001	0.153
Control.3_Transplant1 vs Hot_control.4_Transplant2	1	1.336430279	7.33775774	0.343886074	0.001	0.153
Control.3_Transplant1 vs Control.5_Post-FMT1	1	0.869030126	3.79079171	0.191543207	0.001	0.153
Control.3_Transplant1 vs Treatment.5_Post-FMT1	1	1.126784833	4.96449923	0.248666354	0.001	0.153
Control.3_Transplant1 vs Hot_control.5_Post-FMT1	1	1.449934099	6.74957054	0.296690020	0.001	0.153
Control.3_Transplant1 vs Treatment.6_Post-FMT2	1	1.104217737	5.50651250	0.256039304	0.001	0.153
Control.3_Transplant1 vs Control.6_Post-FMT2	1	0.789637714	3.76981024	0.190685201	0.001	0.153
Control.3_Transplant1 vs Hot_control.6_Post-FMT2	1	1.536951488	7.09904578	0.307330694	0.001	0.153
Treatment.3_Transplant1 vs Hot_control.3_Transplant1	1	0.009634139	0.05096559	0.003905121	1.000	1.000
Treatment.3_Transplant1 vs Control.4_Transplant2	1	1.400930696	7.57729789	0.351169916	0.001	0.153
Treatment.3_Transplant1 vs Treatment.4_Transplant2	1	0.070676505	0.38364693	0.033701584	0.914	1.000
Treatment.3_Transplant1 vs Hot_control.4_Transplant2	1	0.078300724	0.42972488	0.034572356	0.903	1.000
Treatment.3_Transplant1 vs Control.5_Post-FMT1	1	1.367122874	5.79171991	0.292633482	0.002	0.306
Treatment.3_Transplant1 vs Treatment.5_Post-FMT1	1	0.751062093	3.21047535	0.198049426	0.001	0.153
Treatment.3_Transplant1 vs Hot_control.5_Post-FMT1	1	0.541926060	2.46826366	0.149880019	0.008	1.000
Treatment.3_Transplant1 vs Treatment.6_Post-FMT2	1	0.592624022	2.91606971	0.172384588	0.001	0.153
Treatment.3_Transplant1 vs Control.6_Post-FMT2	1	1.523500334	7.13792666	0.337683387	0.001	0.153
Treatment.3_Transplant1 vs Hot_control.6_Post-FMT2	1	0.502178159	2.26737096	0.139381524	0.014	1.000
Hot_control.3_Transplant1 vs Control.4_Transplant2	1	1.529217161	8.39335845	0.358792367	0.001	0.153
Hot_control.3_Transplant1 vs Treatment.4_Transplant2	1	0.067325622	0.37214249	0.030079066	0.922	1.000
Hot_control.3_Transplant1 vs Hot_control.4_Transplant2	1	0.060700684	0.33852115	0.025379211	0.946	1.000
Hot_control.3_Transplant1 vs Control.5_Post-FMT1	1	1.488235610	6.47212491	0.301419861	0.001	0.153
Hot_control.3_Transplant1 vs Treatment.5_Post-FMT1	1	0.831659790	3.65479114	0.207014125	0.001	0.153
Hot_control.3_Transplant1 vs Hot_control.5_Post-FMT1	1	0.615759083	2.86994158	0.160601621	0.002	0.306
Hot_control.3_Transplant1 vs Treatment.6_Post-FMT2	1	0.650193072	3.26217668	0.178630222	0.001	0.153
Hot_control.3_Transplant1 vs Control.6_Post-FMT2	1	1.644765070	7.87562973	0.344280347	0.001	0.153
Hot_control.3_Transplant1 vs Hot_control.6_Post-FMT2	1	0.529144591	2.44579001	0.140193709	0.005	0.765
Control.4_Transplant2 vs Treatment.4_Transplant2	1	1.318966989	7.44875581	0.364264500	0.001	0.153
Control.4_Transplant2 vs Hot_control.4_Transplant2	1	1.461842335	8.31259383	0.372551658	0.001	0.153
Control.4_Transplant2 vs Control.5_Post-FMT1	1	0.837927278	3.74476269	0.189658531	0.001	0.153
Control.4_Transplant2 vs Treatment.5_Post-FMT1	1	1.237187346	5.59522781	0.271675937	0.001	0.153
Control.4_Transplant2 vs Hot_control.5_Post-FMT1	1	1.592357282	7.60689288	0.322231855	0.001	0.153
Control.4_Transplant2 vs Treatment.6_Post-FMT2	1	1.205873137	6.18264781	0.278715502	0.001	0.153
Control.4_Transplant2 vs Control.6_Post-FMT2	1	0.632526126	3.10098856	0.162347020	0.001	0.153
Control.4_Transplant2 vs Hot_control.6_Post-FMT2	1	1.582795172	7.50092755	0.319175808	0.001	0.153
Treatment.4_Transplant2 vs Hot_control.4_Transplant2	1	0.012314222	0.07128989	0.006439167	1.000	1.000
Treatment.4_Transplant2 vs Control.5_Post-FMT1	1	1.342607528	5.78285738	0.307879534	0.001	0.153
Treatment.4_Transplant2 vs Treatment.5_Post-FMT1	1	0.747258352	3.25510805	0.213378236	0.001	0.153
Treatment.4_Transplant2 vs Hot_control.5_Post-FMT1	1	0.553729649	2.58255490	0.165733727	0.001	0.153
Treatment.4_Transplant2 vs Treatment.6_Post-FMT2	1	0.566094860	2.87613678	0.181160998	0.001	0.153
Treatment.4_Transplant2 vs Control.6_Post-FMT2	1	1.445460896	6.95533179	0.348545034	0.001	0.153
Treatment.4_Transplant2 vs Hot_control.6_Post-FMT2	1	0.460097406	2.12533559	0.140514938	0.006	0.918
Hot_control.4_Transplant2 vs Control.5_Post-FMT1	1	1.463793289	6.44782331	0.315330547	0.001	0.153
Hot_control.4_Transplant2 vs Treatment.5_Post-FMT1	1	0.828827179	3.69648712	0.221393105	0.001	0.153
Hot_control.4_Transplant2 vs Hot_control.5_Post-FMT1	1	0.627968770	2.98278398	0.175635749	0.001	0.153
Hot_control.4_Transplant2 vs Treatment.6_Post-FMT2	1	0.613502889	3.15912171	0.184107425	0.001	0.153
Hot_control.4_Transplant2 vs Control.6_Post-FMT2	1	1.570619682	7.68364100	0.354351974	0.001	0.153
Hot_control.4_Transplant2 vs Hot_control.6_Post-FMT2	1	0.469820686	2.21140107	0.136410238	0.006	0.918
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1	1	0.605177838	2.25084909	0.130477583	0.010	1.000
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1	1	1.052890187	4.14363688	0.205704506	0.001	0.153
Control.5_Post-FMT1 vs Treatment.6_Post-FMT2	1	0.890815765	3.71469204	0.188422524	0.001	0.153
Control.5_Post-FMT1 vs Control.6_Post-FMT2	1	0.386092689	1.55217583	0.088432104	0.098	1.000
Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2	1	1.312223689	5.13027257	0.242792541	0.001	0.153
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1	1	0.415007562	1.63726831	0.098409684	0.048	1.000
Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2	1	0.315707871	1.32520255	0.081175259	0.172	1.000
Treatment.5_Post-FMT1 vs Control.6_Post-FMT2	1	1.057952050	4.27000973	0.221588354	0.001	0.153
Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2	1	0.745401493	2.92004928	0.162948730	0.002	0.306
Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2	1	0.437716066	1.94212609	0.108243922	0.010	1.000
Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2	1	1.376659749	5.87527887	0.268580753	0.001	0.153
Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2	1	0.317651641	1.31613667	0.076006369	0.174	1.000
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2	1	1.022748131	4.64833456	0.225119103	0.001	0.153
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2	1	0.501020156	2.20653207	0.121194529	0.002	0.306
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2	1	1.361942386	5.77103132	0.265078454	0.001	0.153

4.3.2.12.3 Phylogenetic

betadisper(beta_div_phylo_all_comparison$S, all_comparison_arrange$time_point) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
           Df Sum Sq  Mean Sq      F N.Perm Pr(>F)    
Groups      5 1.4944 0.298874 25.319    999  0.001 ***
Residuals 143 1.6880 0.011804                         
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              1_Acclimation 2_Antibiotics 3_Transplant1 4_Transplant2 5_Post-FMT1 6_Post-FMT2
1_Acclimation                  1.0000e-03    1.7200e-01    3.7000e-02  1.1100e-01       0.023
2_Antibiotics    3.2520e-07                  1.0000e-03    1.0000e-03  1.0000e-03       0.001
3_Transplant1    1.7307e-01    7.3009e-11                  5.0400e-01  8.3200e-01       0.555
4_Transplant2    3.3595e-02    6.1072e-17    4.8835e-01                6.4500e-01       0.848
5_Post-FMT1      1.1014e-01    7.7616e-12    8.3839e-01    6.4333e-01                   0.720
6_Post-FMT2      3.0349e-02    1.9424e-17    5.4602e-01    8.4889e-01  7.1872e-01            

#adonis2(formula=beta_div_phylo_all_comparison$S ~ time_point*type, data=all_comparison_arrange[labels(beta_div_phylo_all_comparison$S),], permutations=999,strata=all_comparison_arrange$individual) %>%
  #as.matrix() %>%
  #kable()

pairwise <- pairwise.adonis(beta_div_phylo_all_comparison$S, all_comparison_arrange$type_time, perm=999)
pairwise%>%
  tt()

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
Control.1_Acclimation vs Treatment.1_Acclimation	1	0.041869229	0.43916424	0.026714511	0.734	1.000
Control.1_Acclimation vs Hot_control.1_Acclimation	1	0.156094158	2.55468892	0.137684276	0.036	1.000
Control.1_Acclimation vs Control.2_Antibiotics	1	0.861337080	5.65998778	0.273958912	0.001	0.153
Control.1_Acclimation vs Treatment.2_Antibiotics	1	0.913173267	6.56821615	0.319338153	0.002	0.306
Control.1_Acclimation vs Hot_control.2_Antibiotics	1	0.700066804	5.49233852	0.268019119	0.002	0.306
Control.1_Acclimation vs Control.3_Transplant1	1	0.033988546	0.51289646	0.031060357	0.764	1.000
Control.1_Acclimation vs Treatment.3_Transplant1	1	0.215372330	2.72760052	0.163059879	0.033	1.000
Control.1_Acclimation vs Hot_control.3_Transplant1	1	0.231242466	3.05823713	0.169354135	0.015	1.000
Control.1_Acclimation vs Control.4_Transplant2	1	0.028305397	0.51176469	0.030993943	0.694	1.000
Control.1_Acclimation vs Treatment.4_Transplant2	1	0.208435351	2.91338090	0.183077431	0.026	1.000
Control.1_Acclimation vs Hot_control.4_Transplant2	1	0.238647956	3.50464793	0.200212420	0.008	1.000
Control.1_Acclimation vs Control.5_Post-FMT1	1	0.191933673	2.97499219	0.156784896	0.021	1.000
Control.1_Acclimation vs Treatment.5_Post-FMT1	1	0.146272881	1.79073807	0.106650349	0.153	1.000
Control.1_Acclimation vs Hot_control.5_Post-FMT1	1	0.250613478	3.61461852	0.184281867	0.010	1.000
Control.1_Acclimation vs Treatment.6_Post-FMT2	1	0.263223310	4.30602808	0.212056640	0.003	0.459
Control.1_Acclimation vs Control.6_Post-FMT2	1	0.160478949	2.54057422	0.137027807	0.040	1.000
Control.1_Acclimation vs Hot_control.6_Post-FMT2	1	0.255295098	4.01091382	0.200436315	0.002	0.306
Treatment.1_Acclimation vs Hot_control.1_Acclimation	1	0.231088456	4.05218385	0.202081922	0.002	0.306
Treatment.1_Acclimation vs Control.2_Antibiotics	1	0.696872587	4.71383535	0.239113053	0.001	0.153
Treatment.1_Acclimation vs Treatment.2_Antibiotics	1	0.689137599	5.12849058	0.268107437	0.003	0.459
Treatment.1_Acclimation vs Hot_control.2_Antibiotics	1	0.596592947	4.84570105	0.244168802	0.003	0.459
Treatment.1_Acclimation vs Control.3_Transplant1	1	0.105837772	1.70171045	0.096132543	0.151	1.000
Treatment.1_Acclimation vs Treatment.3_Transplant1	1	0.271525858	3.65417744	0.206986559	0.004	0.612
Treatment.1_Acclimation vs Hot_control.3_Transplant1	1	0.286283719	4.01697126	0.211230864	0.002	0.306
Treatment.1_Acclimation vs Control.4_Transplant2	1	0.075042716	1.46463442	0.083862873	0.240	1.000
Treatment.1_Acclimation vs Treatment.4_Transplant2	1	0.276043921	4.14908181	0.241941922	0.015	1.000
Treatment.1_Acclimation vs Hot_control.4_Transplant2	1	0.308698871	4.86600187	0.257924382	0.004	0.612
Treatment.1_Acclimation vs Control.5_Post-FMT1	1	0.263584652	4.36089602	0.214179966	0.007	1.000
Treatment.1_Acclimation vs Treatment.5_Post-FMT1	1	0.253194274	3.27384223	0.179154563	0.041	1.000
Treatment.1_Acclimation vs Hot_control.5_Post-FMT1	1	0.390501202	5.98373926	0.272189330	0.001	0.153
Treatment.1_Acclimation vs Treatment.6_Post-FMT2	1	0.364968924	6.39666658	0.285607975	0.002	0.306
Treatment.1_Acclimation vs Control.6_Post-FMT2	1	0.226282101	3.82922197	0.193110046	0.018	1.000
Treatment.1_Acclimation vs Hot_control.6_Post-FMT2	1	0.348308136	5.84633352	0.267611657	0.002	0.306
Hot_control.1_Acclimation vs Control.2_Antibiotics	1	1.097240771	9.85701700	0.396548669	0.001	0.153
Hot_control.1_Acclimation vs Treatment.2_Antibiotics	1	1.223589280	12.84663319	0.478519340	0.001	0.153
Hot_control.1_Acclimation vs Hot_control.2_Antibiotics	1	0.661505415	7.63879773	0.337420645	0.001	0.153
Hot_control.1_Acclimation vs Control.3_Transplant1	1	0.081539620	2.91655101	0.154179851	0.004	0.612
Hot_control.1_Acclimation vs Treatment.3_Transplant1	1	0.030226267	0.85925828	0.057826458	0.527	1.000
Hot_control.1_Acclimation vs Hot_control.3_Transplant1	1	0.033410268	0.96148331	0.060237716	0.486	1.000
Hot_control.1_Acclimation vs Control.4_Transplant2	1	0.096380142	5.66972138	0.261642560	0.001	0.153
Hot_control.1_Acclimation vs Treatment.4_Transplant2	1	0.035245258	1.44489196	0.100027883	0.224	1.000
Hot_control.1_Acclimation vs Hot_control.4_Transplant2	1	0.032336674	1.33009788	0.086763822	0.262	1.000
Hot_control.1_Acclimation vs Control.5_Post-FMT1	1	0.142033756	5.42002118	0.253035286	0.001	0.153
Hot_control.1_Acclimation vs Treatment.5_Post-FMT1	1	0.096667531	2.36821735	0.136353507	0.022	1.000
Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1	1	0.092525999	2.98249577	0.157118211	0.005	0.765
Hot_control.1_Acclimation vs Treatment.6_Post-FMT2	1	0.100028707	4.38362373	0.215056154	0.001	0.153
Hot_control.1_Acclimation vs Control.6_Post-FMT2	1	0.125775098	5.06012870	0.240270550	0.001	0.153
Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2	1	0.063343780	2.49977367	0.135124554	0.022	1.000
Control.2_Antibiotics vs Treatment.2_Antibiotics	1	0.094083678	0.46354958	0.034429968	0.888	1.000
Control.2_Antibiotics vs Hot_control.2_Antibiotics	1	0.631160740	3.39326529	0.195090757	0.001	0.153
Control.2_Antibiotics vs Control.3_Transplant1	1	1.081539517	9.25761888	0.381637576	0.001	0.153
Control.2_Antibiotics vs Treatment.3_Transplant1	1	1.004005224	7.26097560	0.358372457	0.001	0.153
Control.2_Antibiotics vs Hot_control.3_Transplant1	1	1.080757579	8.28477913	0.371768510	0.001	0.153
Control.2_Antibiotics vs Control.4_Transplant2	1	1.152040544	10.95741891	0.422130526	0.001	0.153
Control.2_Antibiotics vs Treatment.4_Transplant2	1	1.027702743	7.60232276	0.387827650	0.002	0.306
Control.2_Antibiotics vs Hot_control.4_Transplant2	1	1.079920015	8.53201595	0.396247893	0.001	0.153
Control.2_Antibiotics vs Control.5_Post-FMT1	1	1.083638513	9.42638693	0.385909998	0.001	0.153
Control.2_Antibiotics vs Treatment.5_Post-FMT1	1	1.014079699	7.40449675	0.345931831	0.001	0.153
Control.2_Antibiotics vs Hot_control.5_Post-FMT1	1	1.241652163	10.33876224	0.408021597	0.001	0.153
Control.2_Antibiotics vs Treatment.6_Post-FMT2	1	1.183002877	10.62460826	0.414625197	0.001	0.153
Control.2_Antibiotics vs Control.6_Post-FMT2	1	1.135907392	10.00634083	0.400152141	0.001	0.153
Control.2_Antibiotics vs Hot_control.6_Post-FMT2	1	1.225141484	10.74358446	0.417330558	0.001	0.153
Treatment.2_Antibiotics vs Hot_control.2_Antibiotics	1	0.751305380	4.30687517	0.248853426	0.001	0.153
Treatment.2_Antibiotics vs Control.3_Transplant1	1	1.147414607	11.34359994	0.447592290	0.001	0.153
Treatment.2_Antibiotics vs Treatment.3_Transplant1	1	1.144190161	9.39612190	0.439150700	0.003	0.459
Treatment.2_Antibiotics vs Hot_control.3_Transplant1	1	1.209231054	10.55018954	0.447987458	0.001	0.153
Treatment.2_Antibiotics vs Control.4_Transplant2	1	1.175774920	13.26657109	0.486550767	0.001	0.153
Treatment.2_Antibiotics vs Treatment.4_Transplant2	1	1.099595076	9.40628827	0.460950475	0.003	0.459
Treatment.2_Antibiotics vs Hot_control.4_Transplant2	1	1.178403909	10.80153268	0.473719588	0.001	0.153
Treatment.2_Antibiotics vs Control.5_Post-FMT1	1	1.144139048	11.53966642	0.451833091	0.001	0.153
Treatment.2_Antibiotics vs Treatment.5_Post-FMT1	1	1.099646668	9.04159547	0.410206034	0.002	0.306
Treatment.2_Antibiotics vs Hot_control.5_Post-FMT1	1	1.342895837	12.83174099	0.478229907	0.001	0.153
Treatment.2_Antibiotics vs Treatment.6_Post-FMT2	1	1.284614156	13.48281445	0.490590746	0.001	0.153
Treatment.2_Antibiotics vs Control.6_Post-FMT2	1	1.215005290	12.44801755	0.470659758	0.001	0.153
Treatment.2_Antibiotics vs Hot_control.6_Post-FMT2	1	1.268089884	12.91871587	0.479915756	0.001	0.153
Hot_control.2_Antibiotics vs Control.3_Transplant1	1	0.849076966	9.21813905	0.380629537	0.001	0.153
Hot_control.2_Antibiotics vs Treatment.3_Transplant1	1	0.596636472	5.43613272	0.294862963	0.001	0.153
Hot_control.2_Antibiotics vs Hot_control.3_Transplant1	1	0.615692862	5.92195472	0.297257714	0.002	0.306
Hot_control.2_Antibiotics vs Control.4_Transplant2	1	0.876797436	10.90268092	0.420909363	0.001	0.153
Hot_control.2_Antibiotics vs Treatment.4_Transplant2	1	0.674952556	6.47214657	0.350373279	0.003	0.459
Hot_control.2_Antibiotics vs Hot_control.4_Transplant2	1	0.686432726	7.00067496	0.350021935	0.001	0.153
Hot_control.2_Antibiotics vs Control.5_Post-FMT1	1	0.849330865	9.41186993	0.385544817	0.001	0.153
Hot_control.2_Antibiotics vs Treatment.5_Post-FMT1	1	0.752694639	6.81347752	0.327358920	0.002	0.306
Hot_control.2_Antibiotics vs Hot_control.5_Post-FMT1	1	0.843320169	8.84176238	0.370851879	0.001	0.153
Hot_control.2_Antibiotics vs Treatment.6_Post-FMT2	1	0.983612960	11.35445189	0.430836199	0.001	0.153
Hot_control.2_Antibiotics vs Control.6_Post-FMT2	1	0.885529745	9.97205129	0.399328480	0.001	0.153
Hot_control.2_Antibiotics vs Hot_control.6_Post-FMT2	1	0.840123658	9.40607766	0.385398989	0.001	0.153
Control.3_Transplant1 vs Treatment.3_Transplant1	1	0.098602035	2.40012240	0.146347835	0.060	1.000
Control.3_Transplant1 vs Hot_control.3_Transplant1	1	0.111980145	2.78142890	0.156423250	0.010	1.000
Control.3_Transplant1 vs Control.4_Transplant2	1	0.007928377	0.35768341	0.021866385	0.934	1.000
Control.3_Transplant1 vs Treatment.4_Transplant2	1	0.107460204	3.49439587	0.211853523	0.008	1.000
Control.3_Transplant1 vs Hot_control.4_Transplant2	1	0.123295950	4.08042827	0.225682058	0.005	0.765
Control.3_Transplant1 vs Control.5_Post-FMT1	1	0.096104530	3.06336633	0.160693881	0.001	0.153
Control.3_Transplant1 vs Treatment.5_Post-FMT1	1	0.082810318	1.78740344	0.106472895	0.145	1.000
Control.3_Transplant1 vs Hot_control.5_Post-FMT1	1	0.118218149	3.26661338	0.169547876	0.009	1.000
Control.3_Transplant1 vs Treatment.6_Post-FMT2	1	0.136974123	4.89446159	0.234246840	0.001	0.153
Control.3_Transplant1 vs Control.6_Post-FMT2	1	0.097169276	3.23650370	0.168248022	0.007	1.000
Control.3_Transplant1 vs Hot_control.6_Post-FMT2	1	0.152527104	4.99980434	0.238088139	0.001	0.153
Treatment.3_Transplant1 vs Hot_control.3_Transplant1	1	0.001916654	0.03838869	0.002944282	0.981	1.000
Treatment.3_Transplant1 vs Control.4_Transplant2	1	0.138394849	4.84607372	0.257139699	0.001	0.153
Treatment.3_Transplant1 vs Treatment.4_Transplant2	1	0.022693131	0.56103262	0.048527899	0.649	1.000
Treatment.3_Transplant1 vs Hot_control.4_Transplant2	1	0.016177953	0.41465150	0.033400172	0.767	1.000
Treatment.3_Transplant1 vs Control.5_Post-FMT1	1	0.114864381	2.93924035	0.173516656	0.004	0.612
Treatment.3_Transplant1 vs Treatment.5_Post-FMT1	1	0.083463697	1.46603865	0.101343477	0.158	1.000
Treatment.3_Transplant1 vs Hot_control.5_Post-FMT1	1	0.064149535	1.43879911	0.093193719	0.295	1.000
Treatment.3_Transplant1 vs Treatment.6_Post-FMT2	1	0.100463445	2.85333394	0.169303827	0.005	0.765
Treatment.3_Transplant1 vs Control.6_Post-FMT2	1	0.132425349	3.52780737	0.201269177	0.001	0.153
Treatment.3_Transplant1 vs Hot_control.6_Post-FMT2	1	0.069766618	1.83160737	0.115693077	0.106	1.000
Hot_control.3_Transplant1 vs Control.4_Transplant2	1	0.151529980	5.30364409	0.261216364	0.001	0.153
Hot_control.3_Transplant1 vs Treatment.4_Transplant2	1	0.018543667	0.46976985	0.037672696	0.681	1.000
Hot_control.3_Transplant1 vs Hot_control.4_Transplant2	1	0.011324236	0.29624361	0.022280248	0.846	1.000
Hot_control.3_Transplant1 vs Control.5_Post-FMT1	1	0.129157334	3.36426411	0.183196239	0.003	0.459
Hot_control.3_Transplant1 vs Treatment.5_Post-FMT1	1	0.096537959	1.75784138	0.111553438	0.088	1.000
Hot_control.3_Transplant1 vs Hot_control.5_Post-FMT1	1	0.077813129	1.78758462	0.106482538	0.212	1.000
Hot_control.3_Transplant1 vs Treatment.6_Post-FMT2	1	0.115011676	3.30697422	0.180640131	0.001	0.153
Hot_control.3_Transplant1 vs Control.6_Post-FMT2	1	0.146133526	3.95471424	0.208640140	0.001	0.153
Hot_control.3_Transplant1 vs Hot_control.6_Post-FMT2	1	0.066529056	1.77563878	0.105846270	0.103	1.000
Control.4_Transplant2 vs Treatment.4_Transplant2	1	0.123072115	7.12848761	0.354149191	0.001	0.153
Control.4_Transplant2 vs Hot_control.4_Transplant2	1	0.149801593	8.46696532	0.376862883	0.001	0.153
Control.4_Transplant2 vs Control.5_Post-FMT1	1	0.113967223	5.58286749	0.258671258	0.001	0.153
Control.4_Transplant2 vs Treatment.5_Post-FMT1	1	0.106396986	3.07142348	0.169960240	0.020	1.000
Control.4_Transplant2 vs Hot_control.5_Post-FMT1	1	0.165133142	6.54475611	0.290300595	0.001	0.153
Control.4_Transplant2 vs Treatment.6_Post-FMT2	1	0.143231082	8.41195737	0.344583486	0.001	0.153
Control.4_Transplant2 vs Control.6_Post-FMT2	1	0.090154714	4.72894088	0.228132296	0.003	0.459
Control.4_Transplant2 vs Hot_control.6_Post-FMT2	1	0.168581391	8.62389999	0.350224781	0.001	0.153
Treatment.4_Transplant2 vs Hot_control.4_Transplant2	1	0.001788609	0.06719083	0.006071173	0.981	1.000
Treatment.4_Transplant2 vs Control.5_Post-FMT1	1	0.112305287	3.92735591	0.232012367	0.003	0.459
Treatment.4_Transplant2 vs Treatment.5_Post-FMT1	1	0.091039233	1.93447512	0.138826551	0.075	1.000
Treatment.4_Transplant2 vs Hot_control.5_Post-FMT1	1	0.066940234	1.93888992	0.129788085	0.120	1.000
Treatment.4_Transplant2 vs Treatment.6_Post-FMT2	1	0.064927309	2.65796523	0.169751637	0.039	1.000
Treatment.4_Transplant2 vs Control.6_Post-FMT2	1	0.118518130	4.40015430	0.252880188	0.002	0.306
Treatment.4_Transplant2 vs Hot_control.6_Post-FMT2	1	0.022954896	0.83380428	0.060272956	0.537	1.000
Hot_control.4_Transplant2 vs Control.5_Post-FMT1	1	0.131900448	4.67500775	0.250334983	0.001	0.153
Hot_control.4_Transplant2 vs Treatment.5_Post-FMT1	1	0.104293524	2.30585040	0.150651570	0.029	1.000
Hot_control.4_Transplant2 vs Hot_control.5_Post-FMT1	1	0.072523344	2.15076384	0.133167933	0.069	1.000
Hot_control.4_Transplant2 vs Treatment.6_Post-FMT2	1	0.079939737	3.28382512	0.189994118	0.008	1.000
Hot_control.4_Transplant2 vs Control.6_Post-FMT2	1	0.139482909	5.22957940	0.271954955	0.001	0.153
Hot_control.4_Transplant2 vs Hot_control.6_Post-FMT2	1	0.027328390	1.00380807	0.066903553	0.413	1.000
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1	1	0.018425353	0.41441625	0.026884978	0.784	1.000
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1	1	0.059879674	1.73878474	0.098021638	0.105	1.000
Control.5_Post-FMT1 vs Treatment.6_Post-FMT2	1	0.079172437	3.01800458	0.158691968	0.008	1.000
Control.5_Post-FMT1 vs Control.6_Post-FMT2	1	0.043354907	1.53356039	0.087464289	0.203	1.000
Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2	1	0.107830455	3.75004375	0.189875212	0.001	0.153
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1	1	0.032129657	0.64777824	0.041397458	0.691	1.000
Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2	1	0.063935389	1.56518170	0.094486238	0.137	1.000
Treatment.5_Post-FMT1 vs Control.6_Post-FMT2	1	0.052659492	1.22402031	0.075444944	0.289	1.000
Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2	1	0.097535009	2.24024291	0.129942654	0.009	1.000
Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2	1	0.072285454	2.32795928	0.127016830	0.039	1.000
Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2	1	0.117590942	3.55384441	0.181746583	0.001	0.153
Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2	1	0.066672554	1.98595267	0.110416874	0.097	1.000
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2	1	0.059274536	2.38202535	0.129584488	0.024	1.000
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2	1	0.069062800	2.72246021	0.145411456	0.003	0.459
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2	1	0.110817088	4.04365610	0.201742441	0.001	0.153

beta_richness_nmds_all_comparison <- beta_div_richness_all_comparison$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(all_comparison_nmds, by = join_by(sample == newID))

beta_neutral_nmds_all_comparison <- beta_div_neutral_all_comparison$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(all_comparison_nmds, by = join_by(sample == newID))

beta_phylo_nmds_all_comparison <- beta_div_phylo_all_comparison$S %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(all_comparison_nmds, by = join_by(sample == newID))